By looking at the equation of the least-squares regression line, you can see that the correlation

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this question wants us to find the correlation between height and arm span, which is our now, just from this regression equation, we cannot find the exact value because these regression equations are not absolutely perfect. However, we can't know one thing because the slope is positive. That means that when X goes up, why goes up so the two of them positively correlated. So we know that the correct answer is that R is greater than zero, and we are done.

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By looking at the equation of the least-squares regression line, you can see that the correlation between height and arm span is (a) greater than zero. (b) less than zero. (c) 0.93 . (d) 6.4 (e) Can't tell without seeing the data.

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We cannot find the exact correlation between height and arm span because the regression equations are not perfect. The slope is positive and we don't know anything. When X goes up, why goes up so the two of them correlate. We know that the correct answer is greater than zero.

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You have data for many years on the average price of a barrel of oil and the average retail price of a gallon of unleaded regular gasoline. If you want to see how well the price of oil predicts the price of gas, then you should make ascatterplot with ______ as the explanatory variable.

(a) the price of oil
(b) the price of gas
(c) the year
(d) either oil price or gas price
(e) time

the price of oil

In a scatterplot of the average price of a barrel of oil and the average retail price of a gallon of gas, you expect to see
(a) very little association.
(b) a weak negative association.
(c) a strong negative association.
(d) a weak positive association.
(e) a strong positive association.

a strong positive association

If women always married men who were 2 years older than themselves, what would the correlation between the ages of husband and wife be?
(a) 2
(b) 1
(c) 0.5
(d) 0
(e) Can't tell without seeing the data

1

4. The figure below is a scatterplot of reading test scores against IQ test scores for 14 fifth‐grade children. There is one low outlier in the plot. The IQ and reading scores for this child are

(a) IQ = 10, reading = 124.
(b) IQ = 96, reading = 49.
(c) IQ = 124, reading = 10.
(d) IQ = 145, reading = 100.
(e) IQ = 125, reading = 54.5.

IQ = 124, reading =10

If we leave out the low outlier, the correlation for the remaining 13 points in the figure below is closest to
(a) −0.95.
(b) −0.5.
(c) 0.
(d) 0.5.
(e) 0.95.

0.5

The figure below is a scatterplot of reading test scores against IQ test scores for 14 fifth‐grade children. The line is the least‐squares regression line for predicting reading score from IQ score. If another child in this class has IQ score 110,you predict the reading score to be close to

(a) 50.
(b) 60.
(c) 70.
(d) 80.
(e) 90.

60

The slope of the line in the figure below is closest to

(a) −1.
(b) 0.
(c) 1.
(d) 2.
(e) 46.

1

Smokers don't live as long (on average) as nonsmokers, and heavy smokers don't live as long as light smokers. You perform least‐squares regression on the age at death of a group of male smokers y and the number of packs per day they smoked x. The slope of your regression line
(a) will be greater than 0.
(b) will be less than 0.
(c) will be equal to 0.
(d) You can't perform regression on these data.
(e) You can't tell without seeing the data.

will be less than 0

Measurements on young children in Mumbai, India, found this least‐ squares line for predicting height (y) from arm span (x): predicted y = 6.4 + 0.93x. Measurements are in centimeters (cm).
How much does height increase on average for each additional centimeter of arm span?
(a) 0.93 cm
(b) 1.08 cm
(c) 5.81 cm
(d) 6.4 cm
(e) 7.33 cm

.93

Measurements on young children in Mumbai, India, found this least‐ squares line for predicting height (y) from arm span (x): predicted y = 6.4 + 0.93x. Measurements are in centimeters (cm).
According to the regression line, the predicted height of a child with an arm span of 100 cm is about
(a) 106.4 cm.
(b) 99.4 cm.
(c) 93 cm.
(d) 15.7 cm.
(e) 7.33 cm.

99.4

Measurements on young children in Mumbai, India, found this least‐ squares line for predicting height (y) from arm span (x): predicted y = 6.4 + 0.93x. Measurements are in centimeters (cm).
By looking at the equation of the least‐squares regression line, you can see that the correlation between height and arm span is
(a) greater than zero.
(b) less than zero.
(c) 0.93.
(d) 6.4.
(e) Can't tell without seeing the data.

greater than zero

Measurements on young children in Mumbai, India, found this least‐ squares line for predicting height (y) from arm span (x): predicted y = 6.4 + 0.93x. Measurements are in centimeters (cm).
In addition to the regression line, the report on the Mumbai measurements says that r 2= 0.95. This suggests that
(a) although arm span and height are correlated, arm span does not predict height very accurately.
(b) height increases by 0.95 = 0.97 cm for each additional centimeter of arm span.
(c) 95% of the relationship between height and arm span is accounted for by the regression line.
(d) 95% of the variation in height is accounted for by the regression line.
(e) 95% of the height measurements are accounted for by the regression line.

95% of the variation in height is accounted for by the regression line.

Measurements on young children in Mumbai, India, found this least‐ squares line for predicting height (y) from arm span (x): predicted y = 6.4 + 0.93x. Measurements are in centimeters (cm).
One child in the Mumbai study had height 59 cm and arm span 60 cm. This child's residual is
(a) −3.2 cm.
(b) −2.2 cm.
(c) −1.3 cm.
(d) 3.2 cm.
(e) 62.2 cm.

-3.2

A school guidance counselor examines the number of extracurricular activities that students do and their grade point average. The guidance counselor says, "The evidence indicates that the correlation between the number of extracurricular activities a student participates in and his or her grade point average is close to zero." A correct interpretation of this statement would be that

(a) active students tend to be students with poor grades, and vice versa.
(b) students with good grades tend to be students who are not involved in many extracurricular activities, and vice versa.
(c) students involved in many extracurricular activities are just as likely to get good grades as bad grades; the same is true for students involved in few extracurricular activities.
(d) there is no linear relationship between number of activities and grade point average for students at this school.
(e) involvement in many extracurricular activities and good grades go hand in hand.

there is no linear relationship between number of activities and grade point average for students at this school.

The British government conducts regular surveys of household spending. The average weekly household spending (in pounds) on tobacco products and alcoholic beverages for each of 11 regions in Great Britain was recorded. A scatterplot of spending on alcohol versus spending on tobacco is shown below. Which of the following statements is true?
(a) The observation (4.5, 6.0) is an outlier.
(b) There is clear evidence of a negative association between spending on alcohol and tobacco.
(c) The equation of the least‐squares line for this plot would be approximately predicted y = 10 ‐ 2x.
(d) The correlation for these data is r = 0.99.
(e) The observation in the lower‐right corner of the plot is influential for the least‐squares line.

The observation in the lower‐right corner of the plot is influential for the least‐squares line.

The fraction of the variation in the values of y that is explained by the least‐squares regression of y on x is
(a) the correlation.
(b) the slope of the least‐squares regression line.
(c) the square of the correlation coefficient.
(d) the intercept of the least‐squares regression line.
(e) the residual.

the square of the correlation coefficient.

An AP Statistics student designs an experiment to see whether today's high school students are becoming too calculator dependent. She prepares two quizzes, both of which contain 40 questions that are best done using paper‐and‐pencil methods. A random sample of 30 students participates in the experiment. Each student takes both quizzes—one with a calculator and one without—in a random order. To analyze the data, the student constructs a scatterplot that displays the number of correct answers with and without a calculator for each of the 30 students. A least‐squares regression yields the equation

Which of the following statements is/are true?
I. If the student had used Calculator as the explanatory variable, the correlation would remain the same.
II. If the student had used Calculator as the explanatory variable, the slope of the least‐squares line would remain the same.
III. The standard deviation of the number of correct answers on the paper‐and‐pencil quizzes was larger than the standard deviation on the calculator quizzes.
(a) I only
(b) II only
(c) III only
(d) I and III only
(e) I, II, and III

I only

Scientists examined the activity level of fish at 7 different temperatures. Fish activity was rated on a scale of 0 (no activity) to 100 (maximal activity). The temperature was measured in degrees Celsius. A computer regression printout and a residual plot are given below. Notice that the horizontal axis on the residual plot is labeled "predicted (F/T)."

What was the activity level rating for the fish at a temperature of 20.4°C?
(a) 86
(b) 83
(c) 80
(d) 66
(e) 3

83

Scientists examined the activity level of fish at 7 different temperatures. Fish activity was rated on a scale of 0 (no activity) to 100 (maximal activity). The temperature was measured in degrees Celsius. A computer regression printout and a residual plot are given below. Notice that the horizontal axis on the residual plot is labeled "predicted (F/T)."

Which of the following gives a correct interpretation of s in this setting?
(a) For every 1°C increase in temperature, fish activity is predicted to increase by 4.785 units.
(b) The average distance of the temperature readings from their mean is about 4.785°C.
(c) The average distance of the activity level ratings from the least‐squares line is about 4.785 units.
(d) The average distance of the activity level readings from their mean is about 4.785.
(e) At a temperature of 0°C, this model predicts an activity level of 4.785.

The average distance of the activity level ratings from the least‐squares line is about 4.785 units.

Which of these is not true of the correlation r between the lengths in inches and weights in pounds of a sample of brook trout?
(a) r must take a value between −1 and 1.
(b) r is measured in inches.
(c) if longer trout tend to also be heavier, then r > 0.
(d) r would not change if we measured the lengths of the trout in centimeters instead of inches.
(e) r would not change if we measured the weights of the trout in kilograms instead of pounds.

r is measured in inches.

When we standardize the values of a variable, the distribution of standardized values has mean 0 and standard deviation 1. Suppose we measure two variables X and Y on each of several subjects. We standardize both variables and then compute the least‐squares regression line. Suppose the slope of the least‐squares regression line is −0.44. We may conclude that
(a) the correlation will be 1/−0.44.
(b) the intercept will also be −0.44.
(c) the intercept will be 1.0
(d) the correlation will be 1.0.
(e) the correlation will also be −0.44.

the correlation will also be −0.44.

There is a linear relationship between the number of chirps made by the striped ground cricket and the air temperature. A least‐squares fit of some data collected by a biologist gives the model predicted y=25.2+3.3x ,where x is the number of chirps per minute and y is the estimated temperature in degrees Fahrenheit. What is the predicted increase in temperature for an increase of 5 chirps per minute?
(a) 3.3°F
(b) 16.5°F
(c) 25.2°F
(d) 28.5°F
(e) 41.7°F

16.5

Can you find correlation from least squares regression line?

What does the least squares regression line show?

How is correlation related to least squares regression quizlet?

How to determine the equation of the least squares regression line?