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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
Section 4.1

8. Find the least squares regression line for temperature (x) and number of ice cream cones sold per hour (y). x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
Section 4.2

9. Find the least squares regression line for temperature (x) and number of ice cream cones sold per hour (y). x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
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 10

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 11

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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
Section 4.3 12

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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
Section 4.3 13

14. Constant error variance Outlier Patterned residuals None
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. Constant error variance Outlier Patterned residuals None
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. Constant error variance Outlier Patterned residuals None
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. Constant error variance Outlier Patterned residuals None
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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
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 x 65 70 75 80 85 90 95
100
105 y 8 10 11 13 12 16 19 22 23
Section 4.3

20. What percentage people said television was their favorite pastime?
55.0%
59.1%
37.1%
43.8%
Favorite Pastime
Gender
Reading
Television
Music
Male 8 22 20
Female 6 13 11
Section 4.4

21. What percentage people said television was their favorite pastime?
55.0%
59.1%
37.1%
43.8%
Favorite Pastime
Gender
Reading
Television
Music
Male 8 22 20
Female 6 13 11
Section 4.4