1) Data was gathered from several homes for sale in Cincinnati, Ohio, in order to examine the relationship between the size of the house (measured in square feet) and the price of the house (measured in dollars).Suppose you learn the relationship between size and price is linear, positive, and strong, withr= 0.81.You also learn that a regression equation has been constructed in order to predict house price based on house size.Based on this information, which one of the following statements is correct?

A.The units forrwould be "square feet per dollar."

B.The explanatory variable is the house price and the response variable is house size.

C.If we want to predict one variable based on the other, it doesn't matter which variable goes on they-axis when we construct a scatterplot.

D.Sinceris positive, the slope in the regression equation will also be positive.

E.Because the correlation is strong, we should conclude that house size causes a house to have a particular price.

2) Several items from the menu at Starbucks were analyzed in order to determine calorie and carbohydrate content.Is there a relationship between the number of calories in a menu item and the number of grams of carbohydrates in that menu item?When a scatterplot was constructed, the relationship was observed to be linear.The regression equation to predict carbohydrate content based on calorie content was as follows:Predicted grams of carbohydrates = 8.94 + 0.11(number of calories).Which one of the following statements is a correct interpretation of this regression equation?

A.As a number of calories goes up by 1, we predict grams of carbohydrates to increase by 8.94.

B.Because the slope is 0.11, this means the relationship between calories and carbohydrates must be weak.

C.A menu item with 0 grams of carbohydrates is predicted to have 8.94 calories.

D.11% of the variability in grams of carbohydrates can be explained by the regression equation.

E.The predicted grams of carbohydrates for a menu item with 100 calories is 19.94.

3) Consider the following five relationships.We can only use the methods discussed in Chapters 14 and 15 to describeoneof these relationships.For which one relationship would it be appropriate to construct a scatterplot, compute a value ofr, and construct a regression equation?

A.The relationship between age and opinion about COVID-19 vaccinations.

B.The relationship between hours spent sleeping and minutes spent exercising in a typical day.

C.The relationship between a person's political party affiliation and whether or not the person has a tattoo.

D.The relationship between eye color and distance lived from campus.

E.The relationship between weight and favorite ice cream flavor.

4) Can we predict or explain the gestation period (or the length of pregnancy) of a mammal based on longevity (or lifespan)?Gestation period (measured in days) and longevity (measured in years) were examined for a sample of 45 mammals, all of which had lifespans between 1 and 25 years.The correlation between gestation and longevity was found to ber= 0.59, and the regression equation to predict gestation based on longevity was as follows:Predicted gestation = 19.66 + 12.68 (longevity).Based on this information, which one of the following statements is correct?

A.The value ofrwill not change if we decide to measure longevity in months instead of years.

B.If we decide to switch which variable isxand which variable isy, the value ofrwill get bigger.

C.The percentage of variability in the gestation period thatcannotbe explained by the regression equation is approximately 35%.

D.It is appropriate to use the regression equation to predict the gestation period of any mammal with a lifespan between 1 and 100 years.

E.None of the above answer options are correct.

5. Data is collected on the distance of several hikes (in miles), along with the amount of time (in minutes) the hike is expected to take. This data is presented in the following scatterplut, and the regression equation to predict time based on distance is as follows: Predicted time = 1.266 + 31.48 (distance). Which one of the following statements about this data is correct? oon sou Inn A hike that is 2 miles is predicted to take exactly 60 minutes A mathematical error must have been made when constructing the regression equation because if the slope is positive, the intercept should also be positive. If we removed from the analysis the hike that took more than 400 minutes to complete, the correlation between time and distance would get weaker. It would be extrapolation to use the regression equation to predict the time it would take to complete a 6-mile hike. None of the above answer options are correct. pow P1 6. A study found that the number of churches and the number of bars in towns across the United States is positively correlated. From this information, we should conclude that towns with few churches tend to have few bars towns with few churches tend to have many bars. towns with many churches tend to have few bars. people who go to church also like to go to bars. people who go to church tend to stay away from bars. WUOF'?' 7. The scatterplot below shows the relationship between left forearm (LeftArm) and right forearm (RtArm) lengths, both measured in centimeters, for a random sample of college students. Which one of the following answer options must be the regression equation for this data set? 31 30 29 28 27 - 22 24 26 28 30 RtArm A. Predicted left forearm length = 1.22 -0.95 (right forearm length) B. Predicted left forearm length = 1.22 + 0.95 (right forearm length) C. Predicted right forearm length = 1.22 -0.95 (left forearm length) D. Predicted right forearm length = 1.22 + 0.95 (left forearm length) 8. Return to Question 7. Suppose you learn the correlation between left forearm length and right forearm length is r = 0.88. Based on this information, approximately what percentage of the variability in the response variable can be explained by the regression equation? A. 50% B. 88% C. 44% D. 77% E. 94%9. Which one of the following is a true statement? A. If your goal is to predict one variable from another and the explanatory variable is measured in kilograms, the response variable must also be measured in kilograms. B. If you correlate two quantitative variables and find that r = 0.50, this means that half of the values of one variable can be explained by the other variable. . The presence of an outlier always weakens the correlation between two quantitative variables. . It's possible for the value of r-squared to be negative. . A correlation of 4170 is just as strong as a correlation of +0.70. 10. A weather forecaster examines the weather patterns in a random sample of cities in order to better understand how the number of days of rain a city gets per year is related to the number of hours of sunshine that city gets per year. A scatterplot shows a linear relationship between days of rain and hours of sunshine, and the regression equation is as follows: Predicted hours of sunshine = 2847 6.88(days of rain). The regression equation explains 58% of the variability in hours of sunshine. This means the correlation, or r, between days of rain and hours of sunshine must be equal to approximately what value? . 0.76 . 0.76 . 4134 . 0.34 . None of the above answers are correct