3. A clinical psychologist is interested in the relationship between testosterone level in married males and the quality of their marital relationship. A study is conducted in which the testosterone levels of eight married men are measured. The eight men also fill out a standardized questionnaire assessing quality of marital relationships. The questionnaire scale is 0-25 with higher scores indicating better relationships. Testosterone levels are in nanomoles/liter of serum. The data are as follows:

Subject Number	1	2	3	4	5	6	7	8
Relationship Score	24	15	15	10	19	11	20	19
Testosterone Level	12	13	19	25	9	16	15	21

1. What is your slope?
2. What is your intercept?
3. Basedonthedataoftheeightmen,whatrelationshipscorewouldyoupredictforamalewhohasatestosteronelevelof23nanometers/literofserum?Showyourleastsquaresregressionlineequation

2. Regression

A social psychologist conducts a study to determine the relationship between religion and self-esteem. Ten eight graders are randomly chosen for the study. Each individual receives two tests, one measuring self-esteem and the other religious involvement. For the self-esteem test, the lower the score is, the higher self-esteem is; for the test measuring religious involvement, the higher the score is, the higher religious involvement is. The self-esteem test has a range of 1-10 and the religious involvement test ranges from 0-50. The following data are collected.

Subject Religious Involvement Self-esteem

1 5 8

2 25 3

3 45 2

4 20 7

5 30 5

6 40 5

7 1 4

8 15 4

9 10 7

10 35 3

1. Develop an output with a scatter plot and the regression analysis.
2. Write up your results.
3. Usingthisregressionline,whatvalueofself-esteemwouldyoupredictforaneighthgraderwhohadavalueofreligiousinvolvementof43?

Regression Results Write-up

Based upon in-class example

A bivariate regression was preformed to evaluate how well final grade scores in a statistics class could be predicted from pretest scores at the beginning of the semester. The regression analysis was statistically significant, F (1,10) = 6.74 , p=.027, R=.64. The regression equation for predicting final grade from pretest scores was found to be Y = 73.126 + (.476)X. The R² for this equation was .403; that is, 40% of the variance in final grades was predictable from pretest scores.

(Note, you should also include the 95% CI. Because I cannot access SPSS right now, I am not sure what these are on the output.)