1. Logistic Regression An Introduction

2. Uses Designed for survival analysis- binary response For predicting a chance, probability, proportion or percentage. Results are in the form of an odds ratio. Response is bounded with 0≤ p ≤ 1. Provides knowledge of the relationships and strengths among the variables (e.g., smoking 10 packs a day puts you at a higher risk for developing cancer than working in an asbestos mine).

3. Examples Use college ACT or SAT scores to predict whether individuals would receive a grade of B or better in a given math course (to help with placement.) Use various demographic and credit history variables to predict if individuals will be good or bad credit risks. Use various demographic and purchasing information to predict if individuals will purchase from a catalogue sent to their homes. Others?

4. Maximum Likelihood Estimation Complex calculation; statistical programs will run these analyses

5. Interpreting βs The β coefficients estimate the change in the log-odds when x i is increased by 1 unit, holding all other x’s in the model constant. Antilog of the coefficient estimates the odds-ratio estimates the percentage increase (or decrease) in the odds for every 1-unit increase in x i

6. Testing Model Adequacy This is a Chi Square Distribution In Minitab, look for the G and corresponding p-values.