Instructions: Using the attached data set ( gssft.sav ) , conduct a forward LR logistic regression analysis with the following variables:

IV: postlife (belief in life after death), ndegree (degree), nethrs (hrs on internet per week), life (is life exciting or dull), income (total family income), and marital (marital status).

DV: happy

1- C onduct a preliminary linear regression to identify outliers and evaluate multicollinearity among the five continuous variables. Complete the following:

1. Using the Chi square table, identify the critical value at p<001 for identifying outliers. Use EXPLORE to determine if there are outliers. Which cases should be eliminated?
2. Is multicollinearity a problem among the continuous variables?

2) Conduct binary logistic regression using the forward:lr method. Be sure to identify the variable of life as categorical (use the defaults).

1. which variables were entered into the model?
2. To what degree does the model fit the data? Explain.
3. What is the degree of freedom
4. Is the generated model significantly different from the constant-only model?
5. How accurate is the model in predicting DV?
   e) What are the odds ratios for the model variables? Explain

3) Why is using logistic regression in this situation better than linear or multiple regression? Explain

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