65. For this dataset, what is the correlation coefficient (r)?

 

66. What is the regression equation for this dataset?

 

67. Based on the model (i.e. the regression equation) you just made, predict the perceived pleasantness score for a person who has a percent occupancy score of 70.

 

participants	percent occupancy	perceived enjoyment
Taylor	85	6
Lisa	95	8
Brian	35	5
Alec	65	5
Ayesha	70	6
Rachel	50	4.5
Sabah	90	9
Lily	70	7
Dinah	45	2

Grades A Statistics instructor created a linear regression equation to predict students' final exam scores from their midterm exam scores. The regression equation was 

(a) If Susan scores a 70 on the midterm, what did the instructor predict for her score on the final?

(b) Susan got an 80 on the final How big is her residual?

(c) If the standard deviation of the final was 12 points and the standard deviation of the midterm was 10 points, what is the correlation between the two tests?

(d) How many points would someone need to score on the midterm to have a predicted final score of 100?

(1) Below you are given a partial Excel output based on a sample of 25 observations.

Refer to Exhibit 13-3. The estimated regression equation is

(2) You are given the following information about y and x.

Refer to Exhibit 12-1. The sample correlation coefficient equals 

The following estimated regression equation was developed for a model involving two independent variables.

After was dropped from the model, the least-squares method was used to obtain an estimated regression equation involving only as an independent variable.

a. Give an interpretation of the coefficient of in both models.

b. Could multicollinearity explain why the coefficient of differs in the two models? If so, how?