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A survey asked 145 people whether they exercise weekly. Under 30: 22 yes, 43 no. Age 30 or over: 27 yes, 53 no. What proportion of the under-30s exercise weekly?
A researcher measures the sleep of 376 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 288 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 121 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 257 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 244 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 126 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 291 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 133 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 136 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 266 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 168 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 57 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 89 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 193 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 84 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 154 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 338 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 111 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 95 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 231 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 105 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 75 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 68 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 318 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 292 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 98 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 303 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 129 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 325 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 314 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 175 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 438 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 242 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 319 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 144 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 285 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 300 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 328 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 362 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 150 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 483 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 224 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 149 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 310 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 177 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 267 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 151 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 123 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 254 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
A researcher measures the sleep of 219 randomly chosen teenagers and finds a mean of 7.1 hours. What is 7.1?
1. A statistic: it describes the sample
2. A parameter: it describes all teenagers
3. The population
Two fair dice are rolled. Find the probability that the sum is 2.
A random variable X takes the values 0, 1 and 2, with P(X = 0) = 29/100 and P(X = 1) = 13/100. Find P(X = 2).
Many random samples of size 161 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 224 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 490 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 260 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 439 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 410 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 315 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 242 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 409 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 473 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 423 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 230 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 173 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 198 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 58 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 467 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 96 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 195 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 81 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 334 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 91 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 124 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 367 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 487 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 238 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 351 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 113 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 189 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 391 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 382 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 319 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 52 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 220 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 51 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 337 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 316 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 393 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 452 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 102 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 62 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 208 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 95 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 450 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 443 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 75 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 477 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 286 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 374 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 428 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
Many random samples of size 66 are taken from the same population. Which quantity changes from sample to sample?
1. The sample proportion p̂ (a statistic)
2. The population proportion p (a parameter)
3. The sample size
A 95% confidence interval for a proportion is (39/100, 49/100). Find the margin of error.
A company claims that 80% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.80
2. H₀: p < 0.80
3. H₀: p̂ = 0.80
A company claims that 60% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.60
2. H₀: p < 0.60
3. H₀: p̂ = 0.60
A company claims that 62% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.62
2. H₀: p < 0.62
3. H₀: p̂ = 0.62
A company claims that 29% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.29
2. H₀: p < 0.29
3. H₀: p̂ = 0.29
A company claims that 48% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.48
2. H₀: p < 0.48
3. H₀: p̂ = 0.48
A company claims that 71% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.71
2. H₀: p < 0.71
3. H₀: p̂ = 0.71
A company claims that 59% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.59
2. H₀: p < 0.59
3. H₀: p̂ = 0.59
A company claims that 73% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.73
2. H₀: p < 0.73
3. H₀: p̂ = 0.73
A company claims that 66% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.66
2. H₀: p < 0.66
3. H₀: p̂ = 0.66
A company claims that 26% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.26
2. H₀: p < 0.26
3. H₀: p̂ = 0.26
A company claims that 22% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.22
2. H₀: p < 0.22
3. H₀: p̂ = 0.22
A company claims that 27% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.27
2. H₀: p < 0.27
3. H₀: p̂ = 0.27
A company claims that 23% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.23
2. H₀: p < 0.23
3. H₀: p̂ = 0.23
A company claims that 58% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.58
2. H₀: p < 0.58
3. H₀: p̂ = 0.58
A company claims that 28% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.28
2. H₀: p < 0.28
3. H₀: p̂ = 0.28
A company claims that 61% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.61
2. H₀: p < 0.61
3. H₀: p̂ = 0.61
A company claims that 50% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.50
2. H₀: p < 0.50
3. H₀: p̂ = 0.50
A company claims that 43% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.43
2. H₀: p < 0.43
3. H₀: p̂ = 0.43
A company claims that 52% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.52
2. H₀: p < 0.52
3. H₀: p̂ = 0.52
A company claims that 44% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.44
2. H₀: p < 0.44
3. H₀: p̂ = 0.44
A company claims that 65% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.65
2. H₀: p < 0.65
3. H₀: p̂ = 0.65
A company claims that 70% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.70
2. H₀: p < 0.70
3. H₀: p̂ = 0.70
A company claims that 72% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.72
2. H₀: p < 0.72
3. H₀: p̂ = 0.72
A company claims that 63% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.63
2. H₀: p < 0.63
3. H₀: p̂ = 0.63
A company claims that 49% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.49
2. H₀: p < 0.49
3. H₀: p̂ = 0.49
A company claims that 31% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.31
2. H₀: p < 0.31
3. H₀: p̂ = 0.31
A company claims that 56% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.56
2. H₀: p < 0.56
3. H₀: p̂ = 0.56
A company claims that 33% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.33
2. H₀: p < 0.33
3. H₀: p̂ = 0.33
A company claims that 68% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.68
2. H₀: p < 0.68
3. H₀: p̂ = 0.68
A company claims that 21% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.21
2. H₀: p < 0.21
3. H₀: p̂ = 0.21
A company claims that 51% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.51
2. H₀: p < 0.51
3. H₀: p̂ = 0.51
A company claims that 64% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.64
2. H₀: p < 0.64
3. H₀: p̂ = 0.64
A company claims that 24% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.24
2. H₀: p < 0.24
3. H₀: p̂ = 0.24
A company claims that 67% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.67
2. H₀: p < 0.67
3. H₀: p̂ = 0.67
A company claims that 36% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.36
2. H₀: p < 0.36
3. H₀: p̂ = 0.36
A company claims that 25% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.25
2. H₀: p < 0.25
3. H₀: p̂ = 0.25
A company claims that 32% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.32
2. H₀: p < 0.32
3. H₀: p̂ = 0.32
A company claims that 55% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.55
2. H₀: p < 0.55
3. H₀: p̂ = 0.55
A company claims that 53% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.53
2. H₀: p < 0.53
3. H₀: p̂ = 0.53
A company claims that 38% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.38
2. H₀: p < 0.38
3. H₀: p̂ = 0.38
A company claims that 69% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.69
2. H₀: p < 0.69
3. H₀: p̂ = 0.69
A company claims that 74% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.74
2. H₀: p < 0.74
3. H₀: p̂ = 0.74
A company claims that 77% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.77
2. H₀: p < 0.77
3. H₀: p̂ = 0.77
A company claims that 79% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.79
2. H₀: p < 0.79
3. H₀: p̂ = 0.79
A company claims that 46% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.46
2. H₀: p < 0.46
3. H₀: p̂ = 0.46
A company claims that 30% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.30
2. H₀: p < 0.30
3. H₀: p̂ = 0.30
A company claims that 42% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.42
2. H₀: p < 0.42
3. H₀: p̂ = 0.42
A company claims that 37% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.37
2. H₀: p < 0.37
3. H₀: p̂ = 0.37
A company claims that 54% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.54
2. H₀: p < 0.54
3. H₀: p̂ = 0.54
A company claims that 75% of customers are satisfied. A researcher suspects fewer are. Which is the null hypothesis?
1. H₀: p = 0.75
2. H₀: p < 0.75
3. H₀: p̂ = 0.75
A regression on a random sample of 23 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 59 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 46 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 28 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 42 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 35 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 20 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 43 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 81 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 53 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 24 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 21 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 45 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 79 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 78 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 41 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 77 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 48 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 51 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 49 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 87 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 61 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 63 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 55 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 52 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 68 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 73 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 80 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 60 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 76 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 88 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 26 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 62 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 44 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 37 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 70 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 47 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 57 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 71 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 85 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 82 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 56 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 25 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 74 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 86 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 69 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 66 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 40 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 67 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A regression on a random sample of 31 houses gives slope b. What does b estimate?
1. The slope β₁ of the population regression line
2. Nothing: b is the true slope
3. The correlation r
A distribution of incomes has mean 54 thousand dollars and median 43 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 59 thousand dollars and median 54 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 58 thousand dollars and median 46 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 73 thousand dollars and median 59 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 54 thousand dollars and median 46 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 50 thousand dollars and median 44 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 57 thousand dollars and median 49 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 68 thousand dollars and median 53 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 60 thousand dollars and median 55 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 69 thousand dollars and median 58 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 68 thousand dollars and median 57 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 51 thousand dollars and median 46 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 49 thousand dollars and median 41 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 72 thousand dollars and median 60 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 67 thousand dollars and median 58 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 61 thousand dollars and median 56 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 60 thousand dollars and median 52 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 52 thousand dollars and median 45 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 51 thousand dollars and median 42 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 57 thousand dollars and median 46 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 55 thousand dollars and median 41 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 53 thousand dollars and median 48 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 59 thousand dollars and median 47 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 65 thousand dollars and median 51 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 63 thousand dollars and median 55 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 64 thousand dollars and median 56 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 62 thousand dollars and median 49 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 55 thousand dollars and median 43 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 55 thousand dollars and median 44 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 62 thousand dollars and median 48 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 70 thousand dollars and median 60 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 63 thousand dollars and median 58 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 61 thousand dollars and median 54 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 53 thousand dollars and median 44 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 52 thousand dollars and median 44 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 65 thousand dollars and median 53 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 66 thousand dollars and median 58 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 61 thousand dollars and median 55 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 74 thousand dollars and median 59 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 51 thousand dollars and median 40 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 65 thousand dollars and median 60 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 60 thousand dollars and median 54 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 58 thousand dollars and median 48 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 57 thousand dollars and median 50 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 68 thousand dollars and median 55 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 66 thousand dollars and median 54 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 52 thousand dollars and median 42 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 67 thousand dollars and median 52 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 53 thousand dollars and median 41 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
A distribution of incomes has mean 69 thousand dollars and median 55 thousand dollars. What is its most likely shape?
1. Skewed right: a long tail of high values pulls the mean up
2. Skewed left
3. Symmetric
Of 40 drivers who texted, 30 had a near-miss; of 40 who did not text, 10 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 40 drivers
3. Yes, and it proves texting causes near-misses
Of 44 drivers who texted, 33 had a near-miss; of 44 who did not text, 11 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 44 drivers
3. Yes, and it proves texting causes near-misses
Of 20 drivers who texted, 15 had a near-miss; of 20 who did not text, 5 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 20 drivers
3. Yes, and it proves texting causes near-misses
Of 32 drivers who texted, 24 had a near-miss; of 32 who did not text, 8 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 32 drivers
3. Yes, and it proves texting causes near-misses
Of 60 drivers who texted, 45 had a near-miss; of 60 who did not text, 15 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 60 drivers
3. Yes, and it proves texting causes near-misses
Of 28 drivers who texted, 21 had a near-miss; of 28 who did not text, 7 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 28 drivers
3. Yes, and it proves texting causes near-misses
Of 64 drivers who texted, 48 had a near-miss; of 64 who did not text, 16 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 64 drivers
3. Yes, and it proves texting causes near-misses
Of 56 drivers who texted, 42 had a near-miss; of 56 who did not text, 14 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 56 drivers
3. Yes, and it proves texting causes near-misses
Of 48 drivers who texted, 36 had a near-miss; of 48 who did not text, 12 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 48 drivers
3. Yes, and it proves texting causes near-misses
Of 72 drivers who texted, 54 had a near-miss; of 72 who did not text, 18 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 72 drivers
3. Yes, and it proves texting causes near-misses
Of 68 drivers who texted, 51 had a near-miss; of 68 who did not text, 17 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 68 drivers
3. Yes, and it proves texting causes near-misses
Of 76 drivers who texted, 57 had a near-miss; of 76 who did not text, 19 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 76 drivers
3. Yes, and it proves texting causes near-misses
Of 36 drivers who texted, 27 had a near-miss; of 36 who did not text, 9 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 36 drivers
3. Yes, and it proves texting causes near-misses
Of 24 drivers who texted, 18 had a near-miss; of 24 who did not text, 6 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 24 drivers
3. Yes, and it proves texting causes near-misses
Of 52 drivers who texted, 39 had a near-miss; of 52 who did not text, 13 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 52 drivers
3. Yes, and it proves texting causes near-misses
Of 80 drivers who texted, 60 had a near-miss; of 80 who did not text, 20 had one. Is there an association between texting and near-misses in these data?
1. Yes: 75% of texters vs 25% of non-texters had a near-miss
2. No: both groups have 80 drivers
3. Yes, and it proves texting causes near-misses
A school has 600 juniors and 300 seniors. A stratified sample of 90 students is taken in proportion to grade. How many juniors are in the sample?
In a class of 35 students, 12 are girls, 12 play soccer and 2 girls play soccer. A student is chosen at random. Find P(girl or soccer player).
X takes the values 0, 1, 2 with probabilities 51/100, 15/100 and 34/100. Find E(X).
The population proportion is p = 8/10. Find the standard deviation of p̂ for random samples of size 83.
In a random sample of 400, p̂ = 5/10. Using z* = 1.96, find the margin of error of a 95% interval for p.
Test H₀: p = 0.5 with a random sample of 100 in which p̂ = 52/100. Find the z statistic.
Computer output gives slope b = 8/10 with standard error SE_b = 3/100. Find the t statistic for H₀: β₁ = 0.
Find the mean of the data 5, 11, 24, 33, 41.
A scatterplot of car age against resale value has $r = -0.94$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.92$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.90$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.89$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.86$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.91$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.95$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.93$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.87$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.88$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A scatterplot of car age against resale value has $r = -0.85$ . How would you describe the relationship?
1. Strong, negative, linear: older cars tend to be worth less
2. Weak, because r is negative
3. Each extra year causes a fixed loss of value
A news website asks readers to vote online; 4824 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4824 responses are representative
A news website asks readers to vote online; 1714 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1714 responses are representative
A news website asks readers to vote online; 3716 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3716 responses are representative
A news website asks readers to vote online; 1560 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1560 responses are representative
A news website asks readers to vote online; 1035 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1035 responses are representative
A news website asks readers to vote online; 1225 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1225 responses are representative
A news website asks readers to vote online; 866 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 866 responses are representative
A news website asks readers to vote online; 1442 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1442 responses are representative
A news website asks readers to vote online; 3042 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3042 responses are representative
A news website asks readers to vote online; 2939 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2939 responses are representative
A news website asks readers to vote online; 3780 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3780 responses are representative
A news website asks readers to vote online; 3381 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3381 responses are representative
A news website asks readers to vote online; 4312 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4312 responses are representative
A news website asks readers to vote online; 4089 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4089 responses are representative
A news website asks readers to vote online; 2087 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2087 responses are representative
A news website asks readers to vote online; 4745 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4745 responses are representative
A news website asks readers to vote online; 4130 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4130 responses are representative
A news website asks readers to vote online; 1359 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1359 responses are representative
A news website asks readers to vote online; 2941 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2941 responses are representative
A news website asks readers to vote online; 4407 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4407 responses are representative
A news website asks readers to vote online; 4025 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4025 responses are representative
A news website asks readers to vote online; 4086 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4086 responses are representative
A news website asks readers to vote online; 2631 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2631 responses are representative
A news website asks readers to vote online; 598 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 598 responses are representative
A news website asks readers to vote online; 602 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 602 responses are representative
A news website asks readers to vote online; 4358 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4358 responses are representative
A news website asks readers to vote online; 3213 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3213 responses are representative
A news website asks readers to vote online; 3873 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3873 responses are representative
A news website asks readers to vote online; 1872 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1872 responses are representative
A news website asks readers to vote online; 1732 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1732 responses are representative
A news website asks readers to vote online; 4507 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4507 responses are representative
A news website asks readers to vote online; 3855 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3855 responses are representative
A news website asks readers to vote online; 3591 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3591 responses are representative
A news website asks readers to vote online; 1002 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1002 responses are representative
A news website asks readers to vote online; 3188 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 3188 responses are representative
A news website asks readers to vote online; 1545 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1545 responses are representative
A news website asks readers to vote online; 1734 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1734 responses are representative
A news website asks readers to vote online; 1949 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1949 responses are representative
A news website asks readers to vote online; 1077 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1077 responses are representative
A news website asks readers to vote online; 1979 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1979 responses are representative
A news website asks readers to vote online; 4847 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4847 responses are representative
A news website asks readers to vote online; 1616 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1616 responses are representative
A news website asks readers to vote online; 2473 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2473 responses are representative
A news website asks readers to vote online; 1938 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1938 responses are representative
A news website asks readers to vote online; 4351 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4351 responses are representative
A news website asks readers to vote online; 1755 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1755 responses are representative
A news website asks readers to vote online; 4385 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4385 responses are representative
A news website asks readers to vote online; 2748 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 2748 responses are representative
A news website asks readers to vote online; 1328 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 1328 responses are representative
A news website asks readers to vote online; 4228 vote and 80% favor a new law. What is the main problem?
1. Voluntary response bias: people with strong opinions choose to respond
2. The sample is too small
3. None: 4228 responses are representative
A screening test was given to 316 people: 47 had the disease and tested positive, 8 had the disease and tested negative, 34 did not have it and tested positive, 227 did not have it and tested negative. Find P(disease | positive).
X and Y are independent with SD(X) = 27 and SD(Y) = 36. Find SD(X + Y).
A population has standard deviation σ = 9. Find the SD of the sample mean for samples of size 49.
From the same sample of 458, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 100, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 672, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 355, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 705, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 174, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 544, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 255, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 403, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 125, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 418, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 755, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 647, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 524, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 444, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 157, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 369, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 177, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 120, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 668, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 612, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 870, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 883, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 536, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 633, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 275, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 506, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 110, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 610, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 236, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 381, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 529, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 366, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 655, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 604, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 408, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 422, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 285, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 164, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 454, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 622, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 265, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 383, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 357, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 339, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 836, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 578, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 209, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 614, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
From the same sample of 811, a 99% and a 90% interval are computed. Which is wider?
1. The 99% interval
2. The 90% interval
3. They have the same width
At significance level α = 0.05, a test gives P-value 0.04. What do you conclude?
1. Reject H₀: there is convincing evidence for Hₐ
2. Accept H₀
3. Hₐ is proven true
At significance level α = 0.05, a test gives P-value 0.03. What do you conclude?
1. Reject H₀: there is convincing evidence for Hₐ
2. Accept H₀
3. Hₐ is proven true
At significance level α = 0.05, a test gives P-value 0.02. What do you conclude?
1. Reject H₀: there is convincing evidence for Hₐ
2. Accept H₀
3. Hₐ is proven true
At significance level α = 0.05, a test gives P-value 0.01. What do you conclude?
1. Reject H₀: there is convincing evidence for Hₐ
2. Accept H₀
3. Hₐ is proven true
With 30 pairs, b = 2/10 and SE_b = 7/100. Using t* = 2.048 (28 df, 95%), find the upper end of the interval for β₁.
Find the interquartile range (IQR) of the 8 values 3, 9, 14, 17, 24, 27, 33, 46.
$r = 0.6$ , $s_x = 15$ and $s_y = 16$ . Find the slope of the least-squares regression line of $y$ on $x$ .
Researchers randomly assign 66 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 166 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 46 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 133 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 173 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 144 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 40 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 45 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 195 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 189 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 141 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 196 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 112 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 78 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 96 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 169 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 145 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 152 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 129 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 65 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 97 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 70 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 109 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 111 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 51 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 49 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 197 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 188 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 161 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 179 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 155 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 125 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 74 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 177 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 59 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 147 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 124 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 41 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 48 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 93 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 192 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 110 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 200 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 178 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 153 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 84 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 180 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 151 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 194 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Researchers randomly assign 163 volunteers to drink coffee or water, then test their reaction times. What kind of study is it?
1. An experiment: the researchers assign the treatment
2. An observational study
3. A sample survey
Two independent machines fail with probabilities 0.8 and 0.7. Find the probability that both fail.
A free throw is made with probability 1/6. In 5 independent shots, find P(exactly one is made).
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 52 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 73 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 72 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 65 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 83 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 43 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 54 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 70 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 88 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 53 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 95 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 63 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 61 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 92 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 97 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 56 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 64 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 50 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 77 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 69 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 74 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 60 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 47 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 62 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 76 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 93 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 40 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 99 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 49 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 79 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 84 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 42 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 46 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 82 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 57 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 41 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 59 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 100 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 71 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 75 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 81 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 45 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 66 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 58 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 98 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 80 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 96 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 89 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 68 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Household incomes are strongly right-skewed. What is the shape of the sampling distribution of x̄ for random samples of 86 households?
1. Approximately normal, by the central limit theorem
2. Right-skewed like the population
3. Uniform
Using p = 0.5 and z* = 1.96, find the value of n (before rounding up) needed for a margin of error of 0.04.
Test H₀: μ = 39. A random sample of 9 has x̄ = 43 and s = 9. Find the t statistic.
To test whether x is useful for predicting y (from 51 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 88 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 67 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 61 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 68 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 59 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 22 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 47 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 46 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 42 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 87 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 82 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 60 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 64 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 43 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 62 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 89 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 49 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 44 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 73 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 83 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 26 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 30 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 33 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 35 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 80 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 50 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 70 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 71 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 32 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 25 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 79 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 72 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 78 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 69 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 41 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 54 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 65 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 29 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 63 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 23 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 48 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 31 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 37 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 38 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 20 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 21 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 85 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 45 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
To test whether x is useful for predicting y (from 55 pairs), what is the null hypothesis?
1. H₀: β₁ = 0, no linear relationship in the population
2. H₀: b = 0
3. H₀: r = 1
A data set has $Q_1 = 24$ and $Q_3 = 32$ . Above which value is a data point an outlier by the $1.5 \times \text{IQR}$ rule?
$\hat{y} = 30 + 3x$ . A data point is $(7, 46)$ . Find its residual.
Why are 196 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 185 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 189 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 46 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 188 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 47 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 65 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 101 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 66 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 102 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 111 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 85 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 54 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 84 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 75 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 122 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 129 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 61 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 57 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 92 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 132 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 55 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 178 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 96 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 41 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 184 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 182 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 118 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 179 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 154 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 169 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 43 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 97 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 143 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 183 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 76 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 48 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 79 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 151 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 60 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 147 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 78 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 83 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 139 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 155 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 69 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 103 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 115 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 187 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
Why are 198 subjects assigned to the treatments at random?
1. To balance other variables between the groups, so differences can be blamed on the treatment
2. So the results apply to the whole population
3. To give every subject the same chance of being in the study
12% of a population has a condition. A test is positive for 86% of people with the condition and for 11% of people without it. Find P(positive).

Answers

$\frac{22}{65}$
A statistic: it describes the sample
$\frac{1}{36}$
$\frac{29}{50}$
The sample proportion p̂ (a statistic)
$\frac{1}{20}$
H₀: p = 0.80
The slope β₁ of the population regression line
Skewed right: a long tail of high values pulls the mean up
Yes: 75% of texters vs 25% of non-texters had a near-miss
$60$
$\frac{22}{35}$
$\frac{83}{100}$
$\frac{2 \sqrt{83}}{415}$
$\frac{49}{1000}$
$\frac{2}{5}$
$\frac{80}{3}$
$\frac{114}{5}$
Strong, negative, linear: older cars tend to be worth less
Voluntary response bias: people with strong opinions choose to respond
$\frac{47}{81}$
$45$
$\frac{9}{7}$
The 99% interval
Reject H₀: there is convincing evidence for Hₐ
$\frac{1073}{3125}$
$\frac{37}{2}$
$\frac{16}{25}$
An experiment: the researchers assign the treatment
$\frac{14}{25}$
$\frac{3125}{7776}$
Approximately normal, by the central limit theorem
$\frac{2401}{4}$
$\frac{4}{3}$
H₀: β₁ = 0, no linear relationship in the population
$44$
$-5$
To balance other variables between the groups, so differences can be blamed on the treatment
$\frac{1}{5}$