# Writer’s choice

###### Andrew Newman

1. Logistic Regression

a. Explain the logistic regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives

b. Given the data, what approach is taken to construct the model?

c. Explain the effect of multicollinearity in logistic regression, and how multicollinearity is detected?

d. 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

e. How do you interpret the effect of significant coefficients?

f. How are the distribution of the observed residuals of the constructed model tested for normality?

g.What is the coefficient of determination, and what is its significance?

h. How can the regression model be used for prediction?

2. Discriminant Analysis

a. Explain the discriminant analysis model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives

b. Given the data, what approach is taken to construct the model?

c. Explain the effect of multicollinearity in discriminant analysis, and how multicollinearity is detected?

d. 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

e. How do you interpret the effect of significant coefficients?

f. How the distribution of the observed residuals of the constructed model are tested for normality?

g. What is the coefficient of determination, and what is its significance?

h. How can the model be used for prediction?

i. 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.