AP Stats Unit 2 Review

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

• active students tend to be students with poor grades, and vice versa

• students with good grades tend to be students who are not involved in many extracurricular activities, and vice versa

• students involved in many extracurricular activities are just as likely to good grades as bad grades; the same is true for students involved in few extracurricular activities

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

• involvement in many extracurricular activities and good grades go hand in hand

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The fraction of the variation in the values of y that is explained by the least-squares regression of y on x is

• the correlation

• the slope of the least-squares regression line

• the square of the correlation coefficient

• the intercept of the least-squares regression line

• the residual

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CCSS.HSS.ID.C.8

If women always married men who were 2 years older than themselves, what would the correlation between the ages of husband and wife be?

• 2

• 1

• 0.5

• 0

• can't tell without seeing the data

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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 large group of male smokers y and the number of packs per day they smoked x. The slope of your regression line

• will be greater than 0

• will be less than 0

• will be equal to 0

• You can't perform regression on these data

• You can't tell without seeing the data

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CCSS.HSS.ID.C.7

Of those whose favorite sport to watch on television is football, what fraction are males?

• 40/81

• 52/81

• 40/52

• 52/81

• 12/40

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Measurements on young children in Mumbai, India, found this least-squares line for predicting height (cm) y from arm span (cm) x: y-hat = 6.4 + 0.93x

According to the regression line, the predicted height of a child with an arm span of 100 cm is about

• 106.4 cm

• 99.4 cm

• 93 cm

• 15.7 cm

• 7.33 cm

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CCSS.HSS.ID.C.7

If the point with the red x is removed from the scatterplot, what will happen to the slope of the LSRL and the correlation coefficient?

• slope increases, r increases

• slope decreases, r increases

• slope increases, r decreases

• slope decreases, r decreases

• slope is unchanged, r increases

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Does the residual plot support the appropriateness of a linear model?

• Yes because the residual plot has no apparent pattern

• Yes because half the residuals are positive and half are negative

• No because points appear to be randomly distributed

• No because some points are close to the line when others are not

• Yes, because as age increases, the residuals increase

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What can you know from the relative frequency graph?

• The same amount of people were in each continent in 2011 (orange)

• More people were in Asia in 2012

• More people were in North America overall

• More people were in North America in 2010

• A greater percentage of people were in North America than the other continents in 2010

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For a biology project, you measure the weight in grams (g) and the tail length in millimeters (mm) of a group of mice. The equation of the least-squares line for predicting tail length from weight is predicted tail length = 20 + 3*weight Which of the following is NOT correct?

• The slope is 3, which indicates that a mouse's weight should increase by about 3 grams for each additional millimeter of tail length.

• The predicted tail length of a mouse that weighs 39 grams is 137 millimeters.

• By looking at the equation of the least-squares line, you can see that the correlation between weight and tail length is positive.

• If you had measured the tail length in centimeters instead of millimeters, the slope of the regression line would have been 3/10 = 0.3

• One mouse weighed 29 grams and had a tail length of 100 millimeters. The residual for this mouse is -7.

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