Statistical Analysis of Correlation and Regression

2. Determine the type of correlation between the variables. • Positive linear correlation • Negative linear correlation • Nonlinear correlation y x Section 4.1

3. Determine the type of correlation between the variables. • Positive linear correlation • Negative linear correlation • Nonlinear correlation y x Section 4.1

4. Determine the type of correlation between the variables. • Positive linear correlation • Negative linear correlation • Nonlinear correlation y x Section 4.1

5. Determine the type of correlation between the variables. • Positive linear correlation • Negative linear correlation • Nonlinear correlation y x Section 4.1

6. Calculate the linear correlation coefficient r, for temperature (x) and number of ice cream cones sold per hour (y). • 0.946 • 0.973 • –17.694 • 0.383 Section 4.1

7. Calculate the linear correlation coefficient r, for temperature (x) and number of ice cream cones sold per hour (y). • 0.946 • 0.973 • –17.694 • 0.383 Section 4.1

8. Find the least squares regression line for temperature (x) and number of ice cream cones sold per hour (y). Section 4.2

9. Find the least squares regression line for temperature (x) and number of ice cream cones sold per hour (y). Section 4.2

10. The least squares regression line for temperature (x) and number of ice cream cones sold per hour (y) is • Predict the number of ice cream cones sold per hour when the temperature is 88º. • 51.4 • 10.1 • 16.0 • 14.2 Section 4.2

11. The least squares regression line for temperature (x) and number of ice cream cones sold per hour (y) is • Predict the number of ice cream cones sold per hour when the temperature is 88º. • 51.4 • 10.1 • 16.0 • 14.2 Section 4.2

12. The data for temperature (x) and number of ice cream cones sold per hour (y) is shown. • It would be reasonable to use the least squares regression line to predict the number of ice cream cones sold when it is 50 degrees. • True • False Section 4.3

13. The data for temperature (x) and number of ice cream cones sold per hour (y) is shown. • It would be reasonable to use the least squares regression line to predict the number of ice cream cones sold when it is 50 degrees. • True • False Section 4.3

14. Analyze the residual plot and identify which, if any, of the conditions for an adequate linear model are NOT met. • Constant error variance • Outlier • Patterned residuals • None Residual Explanatory Section 4.3

15. Analyze the residual plot and identify which, if any, of the conditions for an adequate linear model are NOT met. • Constant error variance • Outlier • Patterned residuals • None Residual Explanatory Section 4.3

16. Analyze the residual plot and identify which, if any, of the conditions for an adequate linear model are NOT met. • Constant error variance • Outlier • Patterned residuals • None Residual Explanatory Section 4.3

17. Analyze the residual plot and identify which, if any, of the conditions for an adequate linear model are NOT met. • Constant error variance • Outlier • Patterned residuals • None Residual Explanatory Section 4.3

18. Calculate the coefficient of determination r2, for temperature (x) and number of ice cream cones sold per hour (y). • 0.946 • 0.973 • 0.923 • 0.986 Section 4.3

19. Calculate the coefficient of determination r2, for temperature (x) and number of ice cream cones sold per hour (y). • 0.946 • 0.973 • 0.923 • 0.986 Section 4.3

20. What percentage people said television was their favorite pastime? • 55.0% • 59.1% • 37.1% • 43.8% Section 4.4

21. What percentage people said television was their favorite pastime? • 55.0% • 59.1% • 37.1% • 43.8% Section 4.4