1. This past Halloween, data was gathered from several trick-or-treaters about the amount of time they spent trick-or-treating (in minutes) and the amount of candy they obtained (in grams) while trick-or-treating. The relationship between these variables is linear and strong. The regression equation that can be used to predict amount of candy obtained from time spent trick-or-treating is: Predicted grams of candy = 296.90 + 15.45 (minutes spent trick-or-treating) Which one of the following statements is a correct interpretation of this regression equation? A. As minutes spent trick-or-treating goes up by one, we predict grams of candy to increase by 296.90. B. As minutes spent trick-or-treating goes up by one, we predict grams of candy to increase by 15.45. C. As grams of candy goes up by one, we predict minutes spent trick-or-treating to increase by 296.90. D. As grams of candy goes up by one, we predict minutes spent trick-or-treating to increase by 15.45. E. 15.45% of the variability in grams of candy can be explained by the regression equation.

2. Scientists have observed a relationship between the number of times a cricket chirps, per minute, and the temperature, measured in degrees Fahrenheit. As the temperature increases, crickets appear to chirp more, per minute. The relationship between temperature and number of cricket chirps per minute is linear, with r = 0.84, and the equation that can be used to predict temperature based on number of cricket chirps per minute is as follows: Predicted temperature = 25.2 + 3.29 (number of cricket chirps per minute) From the above information, we know that approximately _____% of the variability in temperature can be explained by the regression equation. A. 70.56 B. 84.00 C. 91.65 D. 42.00 E. 16.80

3. Return to Question 6. If the units of temperature were measured in degrees Celsius rather than in degrees Fahrenheit, how would you expect the correlation between temperature and number of cricket chirps per minute to change? A. The correlation would become stronger. B. The correlation would become weaker. C. The correlation would become negative. D. The correlation would become weaker and negative. E. The correlation would not change at all. 8. If there is a linear, strong, and negative relationship between the scores on two tests, we should conclude that A. high scores on one test are associated with high scores on the other test. B. high scores on one test are associated with low scores on the other test. C. the value of r must be close to 0. D. as one test score goes down, the other test score goes down. E. scoring high on one test causes an individual to score low on the other test.

4. Is the height of a building, measured in feet, related to the number of stories the building has? Data was gathered from a random sample of 60 tall buildings in the United States, and this data is presented in the scatterplot below. The correlation between height and number of stories was observed to be r = 0.89, and the equation to predict height based on number of stories is given below. Predicted height = 129.8 + 10.10 (stories) Based on the above information, we know it would be extrapolation to predict the height of a building that has ________ stories. A. 50 B. 10 C. 85 D. 100 E. 22

5. There is a linear relationship between the price of an inkjet printer and the pages per minute that can be printed. The regression equation, which explains 75% of the variability in price, is: Predicted price = -94.22 + 90.88 (pages per minute) This means the correlation, or r, between price and pages per minute must be equal to approximately A. 0.56. B. 0.38. C. 0.87. D. 0.75. E. None of the above answers are correct