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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 survey asked 124 people whether they exercise weekly. Under 30: 21 yes, 50 no. Age 30 or over: 31 yes, 22 no. What proportion of all the people exercise weekly?
A survey asked 140 people whether they exercise weekly. Under 30: 47 yes, 17 no. Age 30 or over: 37 yes, 39 no. What proportion of all the people are under 30 and exercise weekly?
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
In a school, 50 of 125 girls and 100 of 250 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 100 > 50
3. Girls are more likely to walk
In a school, 18 of 45 girls and 36 of 90 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 36 > 18
3. Girls are more likely to walk
In a school, 52 of 130 girls and 104 of 260 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 104 > 52
3. Girls are more likely to walk
In a school, 48 of 120 girls and 96 of 240 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 96 > 48
3. Girls are more likely to walk
In a school, 12 of 30 girls and 24 of 60 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 24 > 12
3. Girls are more likely to walk
In a school, 24 of 60 girls and 48 of 120 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 48 > 24
3. Girls are more likely to walk
In a school, 42 of 105 girls and 84 of 210 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 84 > 42
3. Girls are more likely to walk
In a school, 34 of 85 girls and 68 of 170 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 68 > 34
3. Girls are more likely to walk
In a school, 26 of 65 girls and 52 of 130 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 52 > 26
3. Girls are more likely to walk
In a school, 28 of 70 girls and 56 of 140 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 56 > 28
3. Girls are more likely to walk
In a school, 56 of 140 girls and 112 of 280 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 112 > 56
3. Girls are more likely to walk
In a school, 32 of 80 girls and 64 of 160 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 64 > 32
3. Girls are more likely to walk
In a school, 22 of 55 girls and 44 of 110 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 44 > 22
3. Girls are more likely to walk
In a school, 54 of 135 girls and 108 of 270 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 108 > 54
3. Girls are more likely to walk
In a school, 14 of 35 girls and 28 of 70 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 28 > 14
3. Girls are more likely to walk
In a school, 40 of 100 girls and 80 of 200 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 80 > 40
3. Girls are more likely to walk
In a school, 44 of 110 girls and 88 of 220 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 88 > 44
3. Girls are more likely to walk
In a school, 16 of 40 girls and 32 of 80 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 32 > 16
3. Girls are more likely to walk
In a school, 60 of 150 girls and 120 of 300 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 120 > 60
3. Girls are more likely to walk
In a school, 46 of 115 girls and 92 of 230 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 92 > 46
3. Girls are more likely to walk
In a school, 30 of 75 girls and 60 of 150 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 60 > 30
3. Girls are more likely to walk
In a school, 10 of 25 girls and 20 of 50 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 20 > 10
3. Girls are more likely to walk
In a school, 58 of 145 girls and 116 of 290 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 116 > 58
3. Girls are more likely to walk
In a school, 36 of 90 girls and 72 of 180 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 72 > 36
3. Girls are more likely to walk
In a school, 38 of 95 girls and 76 of 190 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 76 > 38
3. Girls are more likely to walk
In a school, 20 of 50 girls and 40 of 100 boys walk to school. What does this suggest?
1. No association: 40% of each group walks
2. Boys are more likely to walk: 40 > 20
3. Girls are more likely to walk
A survey asked 135 people whether they exercise weekly. Under 30: 44 yes, 40 no. Age 30 or over: 18 yes, 33 no. Find the proportion of under-30s who exercise minus the proportion of those 30 or over who exercise.
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
Heights (cm) and weights (kg) of students have $r = 0.58$ . Heights are converted to inches. What is the new correlation?
1. Still $0.58$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.73$ . Heights are converted to inches. What is the new correlation?
1. Still $0.73$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.94$ . Heights are converted to inches. What is the new correlation?
1. Still $0.94$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.89$ . Heights are converted to inches. What is the new correlation?
1. Still $0.89$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.61$ . Heights are converted to inches. What is the new correlation?
1. Still $0.61$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.54$ . Heights are converted to inches. What is the new correlation?
1. Still $0.54$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.59$ . Heights are converted to inches. What is the new correlation?
1. Still $0.59$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.88$ . Heights are converted to inches. What is the new correlation?
1. Still $0.88$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.57$ . Heights are converted to inches. What is the new correlation?
1. Still $0.57$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.50$ . Heights are converted to inches. What is the new correlation?
1. Still $0.50$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.76$ . Heights are converted to inches. What is the new correlation?
1. Still $0.76$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.90$ . Heights are converted to inches. What is the new correlation?
1. Still $0.90$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.65$ . Heights are converted to inches. What is the new correlation?
1. Still $0.65$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.79$ . Heights are converted to inches. What is the new correlation?
1. Still $0.79$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.86$ . Heights are converted to inches. What is the new correlation?
1. Still $0.86$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.75$ . Heights are converted to inches. What is the new correlation?
1. Still $0.75$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.95$ . Heights are converted to inches. What is the new correlation?
1. Still $0.95$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.71$ . Heights are converted to inches. What is the new correlation?
1. Still $0.71$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.55$ . Heights are converted to inches. What is the new correlation?
1. Still $0.55$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.52$ . Heights are converted to inches. What is the new correlation?
1. Still $0.52$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.92$ . Heights are converted to inches. What is the new correlation?
1. Still $0.92$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.78$ . Heights are converted to inches. What is the new correlation?
1. Still $0.78$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.62$ . Heights are converted to inches. What is the new correlation?
1. Still $0.62$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.83$ . Heights are converted to inches. What is the new correlation?
1. Still $0.83$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.51$ . Heights are converted to inches. What is the new correlation?
1. Still $0.51$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.70$ . Heights are converted to inches. What is the new correlation?
1. Still $0.70$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.77$ . Heights are converted to inches. What is the new correlation?
1. Still $0.77$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.85$ . Heights are converted to inches. What is the new correlation?
1. Still $0.85$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.60$ . Heights are converted to inches. What is the new correlation?
1. Still $0.60$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.84$ . Heights are converted to inches. What is the new correlation?
1. Still $0.84$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.67$ . Heights are converted to inches. What is the new correlation?
1. Still $0.67$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.82$ . Heights are converted to inches. What is the new correlation?
1. Still $0.82$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.80$ . Heights are converted to inches. What is the new correlation?
1. Still $0.80$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.93$ . Heights are converted to inches. What is the new correlation?
1. Still $0.93$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.68$ . Heights are converted to inches. What is the new correlation?
1. Still $0.68$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.56$ . Heights are converted to inches. What is the new correlation?
1. Still $0.56$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.69$ . Heights are converted to inches. What is the new correlation?
1. Still $0.69$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.81$ . Heights are converted to inches. What is the new correlation?
1. Still $0.81$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.87$ . Heights are converted to inches. What is the new correlation?
1. Still $0.87$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.74$ . Heights are converted to inches. What is the new correlation?
1. Still $0.74$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.53$ . Heights are converted to inches. What is the new correlation?
1. Still $0.53$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.63$ . Heights are converted to inches. What is the new correlation?
1. Still $0.63$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.72$ . Heights are converted to inches. What is the new correlation?
1. Still $0.72$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.66$ . Heights are converted to inches. What is the new correlation?
1. Still $0.66$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.91$ . Heights are converted to inches. What is the new correlation?
1. Still $0.91$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
Heights (cm) and weights (kg) of students have $r = 0.64$ . Heights are converted to inches. What is the new correlation?
1. Still $0.64$ : changing units does not change $r$
2. Smaller, since inches are bigger units
3. It cannot be known without the data
$r = 0.6$ , $s_x = 15$ and $s_y = 16$ . Find the slope of the least-squares regression line of $y$ on $x$ .
The least-squares line has slope $2$ , and $\bar{x} = 37$ , $\bar{y} = 52$ . Find its intercept.
The regression line is $\hat{y} = 27 + 9x$ . Predict $y$ for $x = 7$ .
$\hat{y} = 30 + 3x$ . A data point is $(7, 46)$ . Find its residual.
The residual plot of a linear model fitted to 56 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 80 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 67 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 84 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 27 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 64 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 25 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 58 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 43 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 89 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 41 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 88 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 37 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 61 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 82 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 83 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 59 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 26 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 28 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 38 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 48 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 72 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 21 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 39 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 20 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 54 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 47 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 49 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 36 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 77 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 69 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 75 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 66 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 79 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 65 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 87 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 85 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 57 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 44 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 42 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 51 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 24 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 73 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 70 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 71 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 60 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 22 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 90 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 76 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
The residual plot of a linear model fitted to 74 points shows a clear U-shaped curve. What does that tell you?
1. A straight line is not a good model: the relationship is curved
2. The model is good, because residuals are both positive and negative
3. There is one outlier
A point has residual $+8$ . Where is it relative to the regression line?
1. 8 units above the line: the model underpredicts it
2. 8 units below the line
3. On the line
A point has residual $+9$ . Where is it relative to the regression line?
1. 9 units above the line: the model underpredicts it
2. 9 units below the line
3. On the line
A point has residual $+4$ . Where is it relative to the regression line?
1. 4 units above the line: the model underpredicts it
2. 4 units below the line
3. On the line
A point has residual $+7$ . Where is it relative to the regression line?
1. 7 units above the line: the model underpredicts it
2. 7 units below the line
3. On the line
A point has residual $+1$ . Where is it relative to the regression line?
1. 1 units above the line: the model underpredicts it
2. 1 units below the line
3. On the line
A point has residual $+6$ . Where is it relative to the regression line?
1. 6 units above the line: the model underpredicts it
2. 6 units below the line
3. On the line
A point has residual $+2$ . Where is it relative to the regression line?
1. 2 units above the line: the model underpredicts it
2. 2 units below the line
3. On the line
A point has residual $+3$ . Where is it relative to the regression line?
1. 3 units above the line: the model underpredicts it
2. 3 units below the line
3. On the line
A point has residual $+5$ . Where is it relative to the regression line?
1. 5 units above the line: the model underpredicts it
2. 5 units below the line
3. On the line
For trees, $\widehat{\text{height}} = 3 + 8\,\text{age}$ (feet, years). What does $8$ mean?
1. For each extra year of age, the predicted height increases by 8 feet
2. Every tree grows exactly 8 feet each year
3. A tree of age 0 is 8 feet tall
For trees, $\widehat{\text{height}} = 3 + 5\,\text{age}$ (feet, years). What does $5$ mean?
1. For each extra year of age, the predicted height increases by 5 feet
2. Every tree grows exactly 5 feet each year
3. A tree of age 0 is 5 feet tall
For trees, $\widehat{\text{height}} = 3 + 7\,\text{age}$ (feet, years). What does $7$ mean?
1. For each extra year of age, the predicted height increases by 7 feet
2. Every tree grows exactly 7 feet each year
3. A tree of age 0 is 7 feet tall
For trees, $\widehat{\text{height}} = 3 + 2\,\text{age}$ (feet, years). What does $2$ mean?
1. For each extra year of age, the predicted height increases by 2 feet
2. Every tree grows exactly 2 feet each year
3. A tree of age 0 is 2 feet tall
For trees, $\widehat{\text{height}} = 3 + 9\,\text{age}$ (feet, years). What does $9$ mean?
1. For each extra year of age, the predicted height increases by 9 feet
2. Every tree grows exactly 9 feet each year
3. A tree of age 0 is 9 feet tall
For trees, $\widehat{\text{height}} = 3 + 3\,\text{age}$ (feet, years). What does $3$ mean?
1. For each extra year of age, the predicted height increases by 3 feet
2. Every tree grows exactly 3 feet each year
3. A tree of age 0 is 3 feet tall
For trees, $\widehat{\text{height}} = 3 + 4\,\text{age}$ (feet, years). What does $4$ mean?
1. For each extra year of age, the predicted height increases by 4 feet
2. Every tree grows exactly 4 feet each year
3. A tree of age 0 is 4 feet tall
For trees, $\widehat{\text{height}} = 3 + 6\,\text{age}$ (feet, years). What does $6$ mean?
1. For each extra year of age, the predicted height increases by 6 feet
2. Every tree grows exactly 6 feet each year
3. A tree of age 0 is 6 feet tall
A regression has $r = 0.2$ . What fraction of the variation in $y$ is explained by the linear model ( $r^2$ )?
A model for children aged 2 to 6 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 12 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 13 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 9 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 15 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 8 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 5 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 10 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 11 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 14 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever
A model for children aged 2 to 7 predicts height from age. Is it reasonable to use it for a 40-year-old?
1. No: 40 is far outside the ages used to fit the model (extrapolation)
2. Yes, if r is close to 1
3. Yes: a line can be extended forever

Answers

$\frac{22}{65}$
$\frac{13}{31}$
$\frac{47}{140}$
Yes: 75% of texters vs 25% of non-texters had a near-miss
No association: 40% of each group walks
$\frac{61}{357}$
Strong, negative, linear: older cars tend to be worth less
Still $0.58$ : changing units does not change $r$
$\frac{16}{25}$
$-22$
$90$
$-5$
A straight line is not a good model: the relationship is curved
8 units above the line: the model underpredicts it
For each extra year of age, the predicted height increases by 8 feet
$\frac{1}{25}$
No: 40 is far outside the ages used to fit the model (extrapolation)