Unit 6 Multiple Regression Assignment 6 Calculating and interpreting Multiple Regression: For this assignment you will be presented with a data set that includes 4 variables. The data set contains...

Unit 6 Multiple Regression

Assignment 6

Calculating and interpreting Multiple Regression:

For this assignment you will be presented with a data set that includes 4 variables.

The data set contains four variables:

1. Self-Reported Criminality (Number of times a person has committed a crime in the past month.


2. Anti-Social Personality Score (A score from a psychological test designed to measure anti-social personality). The score ranges from 1-10. With a higher number representing a higher level of antisocial personality) Meaning those who score low do not have an antisocial personality, but high scores may.


3. Age (The respondents current age in years).


4. Social Class (The respondents social class: 1= lower class, 2 = middle class, 3 = upper class).Since this is a categorical variable it will need to be dummy coded. However, I already did this for you! So you do not need to recode.Remember your interpretation of b will be different than it is for the continuous variables.
   
   

Your assignment is to run and interpret the results form a regression with self-reported criminality as the outcome variable, and Anti-Social Personality Score, Age, and Social Class as the predictor variables. You should use the forced entry method for the regression.

Data Set

You can Download the data set to use for this assignmentUnit 5.4 Homework Data.sav.

Using this data please do the following:

Imagine that you hypothesize the following:

• There is a positive relationship between antisocial personality scores and criminality.


• There is an inverse relationship between age and criminality.


• Lower class individuals commit more criminality than upper and middle class individuals.

Use multiple regression to test these hypotheses while controlling for the other variables. In other words run a regression with all of these variables included.

Please do the following.

1. First answer the following question. What would be the benefits of conducting a multiple regression rather than simply running more than one bivariate regression?


2. Run a multiple regression with all of the variables in the data set included:
   
   
   1. You should test the assumptions and report your findings: Make sure to check the following assumptions. For the assignment test each assumption and report the results. Indicate whether the data meets each of the following assumptions and diagnostic tools:
      
      
      1. Outliers
      
      
      2. No Zero Cells
      
      
      3. Linearity
      
      
      4. Level of Measurement
      
      
      5. Normally Distributed Error Term
      
      
      6. No Auto Correlation
      
      
      7. Multicollinearity
      
      
      
      
   
   
   2. For this second part assume that you met all of the assumptions to run a multiple regression (even if you did not). Report the results for the regression you ran as you would report them in a journal article. Make sure you include all relevant information. You should report the results for the following:
      
      
      1. The F test (And its Sig value)
      
      
      2. R²
         
      
      
      3. B (slope) with significance for each variable
      
      
      4. Beta for each variable (Rank order the Betas)
      
      
      
      
   
   
   
   

Note: Like the previous assignments the more detail you provide the better. If you produce any tables or graphs you should consider cutting and pasting them into your .rtf document.

As usual you should summit it as a word doc or .rtf file.