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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
The residual plot of a regression on 85 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 38 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 47 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 35 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 75 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 51 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 55 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 65 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 42 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 64 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 87 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 71 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 27 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 72 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 37 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 44 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 24 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 61 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 60 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 70 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 78 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 53 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 68 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 63 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 31 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 76 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 29 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 30 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 46 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 48 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 36 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 79 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 41 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 82 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 21 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 90 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 84 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 23 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 33 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 22 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 62 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 77 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 39 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 45 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 67 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 57 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 52 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 49 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 66 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
The residual plot of a regression on 88 points fans out: residuals spread more as x grows. Which condition fails?
1. Equal variance of the residuals
2. Linearity
3. Normality of the population
Computer output gives slope b = 8/10 with standard error SE_b = 3/100. Find the t statistic for H₀: β₁ = 0.
A regression uses 41 data pairs. How many degrees of freedom does the t test for the slope have?
In regression output, the row for x reads: Coef 0.97, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.96, SE Coef 0.07, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.14, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.17, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.55, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.79, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.66, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.77, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.99, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.42, SE Coef 0.03, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.93, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.30, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.62, SE Coef 0.02, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.12, SE Coef 0.02, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.98, SE Coef 0.07, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.71, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.68, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.40, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.80, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.20, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.78, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.91, SE Coef 0.02, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.52, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.57, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.95, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.40, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.47, SE Coef 0.02, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.67, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.40, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.51, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.45, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.12, SE Coef 0.07, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.79, SE Coef 0.07, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.36, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.85, SE Coef 0.04, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.41, SE Coef 0.02, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.38, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.55, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.44, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.99, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.46, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.56, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.11, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.65, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.85, SE Coef 0.09, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.64, SE Coef 0.08, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.29, SE Coef 0.07, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.79, SE Coef 0.05, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.98, SE Coef 0.01, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
In regression output, the row for x reads: Coef 0.21, SE Coef 0.06, and a t value. Which number is the slope's standard error?
1. The SE Coef in the x row
2. The Coef in the x row
3. The value s (the SD of the residuals)
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.
A 95% interval for the slope β₁ is (0.4, 0.8). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.2, 0.6). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.4, 0.6). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.1, 0.7). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.1, 0.9). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.3, 0.8). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.1, 0.8). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.3, 0.5). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.4, 0.7). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.2, 0.7). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.4, 0.9). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.3, 0.9). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.2, 0.9). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.2, 0.5). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.1, 0.6). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.2, 0.8). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.4, 0.5). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.3, 0.7). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.1, 0.5). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
A 95% interval for the slope β₁ is (0.3, 0.6). What does it suggest?
1. Convincing evidence of a positive linear relationship: 0 is not in the interval
2. No evidence of a relationship
3. x causes y to increase
SE_b = 1/100 and t* = 2.048. Find the width of the 95% confidence interval for β₁.
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
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.006. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.005. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.004. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.009. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.001. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.008. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.007. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.002. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
An observational study tests H₀: β₁ = 0 for hours of sleep (x) and test score (y). The P-value is 0.003. What can you conclude?
1. Convincing evidence of a linear association between sleep and scores in the population sampled
2. More sleep causes higher scores
3. The correlation must be close to 1
Output shows slope b = 6/10 and t = 9 for H₀: β₁ = 0. Find SE_b.

Answers

The slope β₁ of the population regression line
Equal variance of the residuals
$\frac{80}{3}$
$39$
The SE Coef in the x row
$\frac{1073}{3125}$
Convincing evidence of a positive linear relationship: 0 is not in the interval
$\frac{128}{3125}$
H₀: β₁ = 0, no linear relationship in the population
Convincing evidence of a linear association between sleep and scores in the population sampled
$\frac{1}{15}$