A public opinion consultant is interested in the demographics of those who are in favor of capital punishment (death penalty).
Data set: Ch 12 – Exercise 03B.sav
Codebook
Variable: Death_penalty
Definition: [Outcome] Are you in favor of the death penalty?
Type: Continuous (1 = Anti-death penalty . . . 10 = Prodeath penalty)
Variable: Age
Definition: [Predictor] Age
Type: Continuous
Variable: Gender
Definition: [Predictor] Gender
Type: Categorical (0 = Female, 1 = Male)
Variable: Race
Definition: [Predictor] Race
Type: Categorical (0 = African American, 1 = Asian, 2 = Caucasian, 3 = Latino, 4 = Other)
Variable: Religion
Definition: [Predictor] Religion

Type: Categorical (0 = Atheist, 1 = Buddhist, 2 = Catholic, 3 = Hindu, 4 = Jewish, 5 = Other)
Variable: Education
Definition: [Predictor] Years of education (High school = 12, Associate’s = 14, Bachelor’s = 16, Master’s = 18,
Doctorate > 18)
Type: Continuous
This data set contains two polychotomous predictor variables (Race and Religion), which are represented by the corresponding dummycoded variables. Follow this load procedure:
1. Move Death_penalty into the Dependent box.
2. Move Age, Gender, and Education into the Independent(s) box.
3. Set Method to Forward.
4. Click Next.
5. Move Race.1, Race.2, Race.3, and Race.4 into the Independent(s) box.
6. Click Next.
7. Move Religion.1, Religion.2, Religion.3, Religion.4, and Religion.5 into the Independent(s) box.

a. Write the hypotheses.
b. Run each criterion of the pretest checklist (sample size, normality, multicollinearity) and discuss your findings.
c. Run the multiple regression analysis and document your findings (overall R², R² change of statistically significant predictors, hypotheses resolution).
d. Write an abstract under 200 words detailing a summary of the study, the multiple regression analysis results, hypothesis resolution, and implications of your findings.

The B data set is the same as the A data set with the following modifications:
The Education variable has been recoded from a continuous variable (total number of years of education) to a categorical variable (0 = No college degree, 1 = College degree)