PPT-Regression Models

Professor William Greene Stern School of Business IOMS Department Department of Economics Regression and Forecasting Models Part 8 Multicollinearity Diagnostics Multiple Regression Models. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Statistics and Data Analysis. Part . 10 . – . Qualitative Data. Modeling Qualitative Data. A Binary Outcome. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models . Part . 7 . – . Multiple Regression. Analysis. Model Assumptions. Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 1 . – . Simple Linear Model. Theory. Demand Theory: Q = f(Price). Advanced Models and Methods . in Behavioral Research. Chris Snijders. c.c.p.snijders@gmail.com. 3 ects. http://www.chrissnijders.com/ammbr (=studyguide). literature: Field book + separate course material. Operations Research . and Control Systems . in Health Care. Spring/Summer 2016. Forecasting - Introduction. Forecasting in Health Care. Forecasting Models. Structural Models. Time Series Models. Expert Judgment. Chapter 3 – Exploring Data. Day 3. Regression Line. A straight line that describes how a . _________ . variable, . __. ,. . changes as an . ___________ variable. , . ___. ,. . changes. used to . __________ . Professor William Greene. Stern School of Business. IOMS Department. Department of Economics. Regression and Forecasting Models. Part . 2 . – . Inference About the. Regression. The Linear Regression Model. Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Regression and Forecasting Models. Part 0 - Introduction. . Professor William Greene; . Economics . and IOMS Departments. Used for a variety of purposes, including prediction, data reduction, and causal inference.. From experiments and observational studies.. Slide . 2. Hierarchical Data. Data structures are often hierarchical or “nested”. Frank Wood, fwood@stat.columbia.eduLinear Regression Models Lecture 4, Slide 2Today: Normal Error Regression Model Frank Wood fwoodstatcolumbiaeduLinear Regression Models Lecture 3 Slide 2Least Squares MaxminimizationFunction to minimize wrt Minimize this by maximizing QFind partials and set both equal to zero go Lecture Outline. 1. Simple Regression:. . Predictor variables Standard Errors. Evaluating Significance of Predictors . Hypothesis Testing. How well do we know . ?. How well do we know . ?. Multiple Linear Regression: . 1. 2. Office Hours. :. More office hours, schedule will be posted soon.. . On-line office hours are for everyone, please take advantage of them.. . Projects:. Project guidelines and project descriptions will be posted Thursday 9/25.. Regression Trees. Characteristics of classification models. model. linear. parametric. global. stable. decision tree. no. no. no. no. logistic regression. yes. yes. yes. yes. discriminant. analysis.