A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.

This dataset has a binary response (outcome, dependent) variable called admit. A value of 1 indicates admission was granted. There are three predictor variables: gre, gpa, and rank. We will treat the variables gre and gpa as continuous. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest.

Do a standard logistic regression analysis to see how the variables predict admission into graduate school. Don’t just cut and paste tables from R to answer the questions. Explain all your findings!
1-Provide the summary statistics (including standard deviation) for all variables.
2- Perform a regression model using rank as is, versus creating dummy variables in R (hint: try mydata$rank <- factor(mydata$rank) when you want to create dummy variables, and just "rank" in your regression model).
3-What does the regression model say about how much the predictor variables influence the admission?
4-Which of the indpendent variables are the most predictive?
5-Interpret the coefficents in their logit form and then exponentiate the coefficients (exp(b)) and discuss the meaning of each coefficient.
6-What variables do and do not significantly predict admission?

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