A researcher collected data from 225 college students regarding college performance variables. The data comprised males and females along with their c

A researcher collected data from 225 college students regarding college performance variables. The data comprised males and females along with their college classification, races who completed an inventory reflecting memory scores at various interventions, study hours, GRE score, personality trait, psych graduate admission, abortion position, academic probation status, self-efficacy, anxiety score, and GPA. For this project, the dependent or response variable is GPA. In contrast, the independent (IV) or explanatory variables are self-efficacy, study hours, and anxiety scores before therapy. How well do the explanatory variables (IV) predict GPA? Conduct a multiple regression analysis. Use the SPSS data set provided to examine the relationship between the independent variables (predictors) and the dependent variable. A reasonable model might propose that GPA is a function of the explanatory variables (IVS). In using SPSS regression to evaluate the model outlined here, interpret the relevant SPSS outputs and, in particular, discuss the amount of variance accounted for by the set of predictors, the significance of the relationship between the set of predictors (independent variables), and the dependent or response variable, and the relative importance of the predictors or independent variables.  After running the analysis in SPSS, interpret the SPSS outputs and write a report using the following as guidelines:  You must include the following in your report (the report must be in sentences, not bullets): * Include a table (must have a number and a descriptive title: remember that SPSS tables are not tables for your paper, and graph (s) to provide illumination. Table (s) and charts must be referenced or cited in your report.  * The report must include technical details: 1. Sample Description and Sample Size 2. Research questions and hypothesis 3. Means, standard deviations, skewness, and kurtosis of study variables 4. Multiple regression assumptions and assessment (diagnostics) 5. Test statistics (e.g., F-ratio) and other related test statistics (e.g., regression coefficients, part and partial correlations, and collinearity diagnostics) 6. P-values 7. Degrees of freedom 8. Confidence intervals of the regression weights (optional) 9. Conclusion (technical details) Your written report must be concise, and the assignment submission should be 3 – 6 pages long. It should contain proper grammar, be free of spelling errors, and reflect critical thinking.  Interpretations of Findings For all projects, including this one, our criterion alpha or significance level is .05. Any significance test with .05 or less is considered significant. Thus, the null hypothesis should be rejected. Note: Average the VIF for the collinearity statistics and note on your report.