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Logistic Forward Multiple Regression Analysis Using SPSS

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Logistic Regression: Discovering Statistics (4th ed) Using IBM SPSS Statistics Chapter 19, "Logistic Regression", and using IBM SPSS Statistics the following:

1. State the underlying assumptions for the statistical test.

2. State whether the assumptions have been met.

a. If the assumptions were not met (either in actuality or hypothetically), state what alternatives are available.

3. State the null and alternative (research) hypotheses.

4. Input syntax file

5. Create a MS word document with the Output file in it.

6. Create a results table consistent with requirements of APA.

7. Report the results for the following (where applicable) using correct APA format.
a. For ANOVA, ANCOVA, and Repeated Measures ANOVA models, provide interpretations for the main effects and interactions as well as any post-hoc tests.

b. For Multiple and Logistic Regression models include appropriate measures of model fit as well as the specific procedure used (e.g., Hierarchical, Enter, Stepwise, Forward, Backward).

8. Describe how to compute the sample size to achieve 80% power, alpha = .05, and the appropriate effect size.

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This solution is comprised of a detailed explanation of Multiple Logistic regression analysis using SPSS. This solution mainly discussed the Multiple Logistic regression analysis with the help of SPSS software. The solution provides the interpretation of SPSS output.

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See attached for tables.

1. State the underlying assumptions for the statistical test.
The assumptions of conducting logistic regression are:
1. The observations should be independent.
2. The independent variables should not be correlated with each other.
3. The dependent variable should be binary.
4. The independent variables should be measured without error.
2. State whether the assumptions have been met.
The above all assumptions are met for logistic regression. If the assumptions were not met for example the independent variables are related with each other, then we can either drop that variable which is affecting other variables. Logistic Regression is applied only when our dependent variable is categorical or binary, here our dependent variable is a binary variable.

a. If the assumptions were not met (either in actuality or hypothetically), state what alternatives are available.
3. State the null and alternative (research) hypotheses.

Null Hypothesis (Ho): The model ...

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