This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Logistic Regression Data Mining Prof. Dawn Woodard School of ORIE Cornell University 1 Outline 1 Announcements 2 Logistic Regression 2 Exam 1 Calculator required No cell phones, iPods, etc. may be looked at during the exam. Bring your watch. We will begin exactly at 1:25 and end exactly at 2:15; arrive 5 minutes early! We have not used very many equations so I expect you to remember the ones we have used. Are there any particular equations you would like me to provide? 4 Multiple Logistic Regression With a single predictor, we use the (logistic regression) model: Pr ( Y = 1 | X = x ) = L ( β + β 1 x ) What model should we use when we have multiple predictors X 1 , . . . , X P ? 6 Multiple Logistic Regression When we have multiple predictors, we use the (multiple) logistic regression model: Pr ( Y = 1 | X 1 , . . . , X P ) = L ( β + β 1 X 1 + . . . + β P X P ) This can be written as: log ÿ Pr ( Y = 1 | X 1 , . . . , X P ) 1 − Pr ( Y = 1 | X 1 , . . . , X P ) = β + β 1 X 1 + . . .....
View Full Document
This note was uploaded on 12/23/2009 for the course ORIE 4740 at Cornell.