Questions: Please answer each of the following questions and provide examples for better clarity wherever applicable. Provide in-text citations. In answering the following questions please include choice of significance level and the effect of p values 1 Logistic Regression Explain the logistic regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives Given the data, what approach is taken to construct the model? Explain the effect of multicollinearity in logistic regression, and how multicollinearity is detected? Explain the hypotheses on coefficients of the regression and how the results of testing these hypotheses are interpreted about significance of these coefficients? Include both unidirectional and bidirectional situations How do you interpret the effect of significant coefficients? How are the distribution of the observed residuals of the constructed model tested for normality?  What is the coefficient of determination, and what is its significance? How can the regression model be used for prediction? 2 Discriminant Analysis Explain the discriminant analysis model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives Given the data, what approach is taken to construct the model? Explain the effect of multicollinearity in discriminant analysis, and how multicollinearity is detected? Explain the hypotheses on coefficients of the model and how the results of testing these hypotheses are interpreted about significance of these coefficients? Include both unidirectional and bidirectional situations How do you interpret the effect of significant coefficients? How the distribution of the observed residuals of the constructed model are tested for normality?  What is the coefficient of determination, and what is its significance? How can the model be used for prediction? What is the confusion matrix in discriminant analysis? In answering the above questions, you can provide examples formulated according to the model’s setting without actually constructing the solution.

Some commonly employed statistical analyses include correlation and regression. In this assignment, you will practice correlation and regression techniques from an SPSS data set.General Requirements:Use the following information to ensure successful completion of the assignment:Review "SPSS Access Instructions" for information on how to access SPSS for this assignment.Access the document, "Introduction to Statistical Analysis Using IBM SPSS Statistics, Student Guide" to complete the assignment.Download the file "Bank.sav" and open it with SPSS. Use the data to complete the assignment.Download the file "Census.sav" and open it with SPSS. Use the data to complete the assignment.Directions:Locate the data set "Bank.sav" and open it with SPSS. Follow the steps in section 10.15 Learning Activity as written. Answer all of the questions in the activity based on your observations of the SPSS output. Type your answers into a Word document and include supporting graphs or tables from the SPSS output for submission to the instructor.Locate the data set "Census.sav" and open it with SPSS. Follow the steps in section 11.16 Learning Activity as written. Answer all of the questions in the activity based on your observations of the SPSS output. Type your answers into a Word document and include supporting graphs or tables from the SPSS output for submission to the instructor.