Part 0 -- Introduction Statistical Inference and Regression Analysis: Stat-GB.3302.30, C22.0015.01 Professor William Greene Stern School of Business IOMS.

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Part 0 -- Introduction Statistical Inference and Regression Analysis: Stat-GB , C Professor William Greene Stern School of Business IOMS Department Department of Economics

Part 0 -- Introduction Statistical Inference and Regression Analysis Part 0 - Introduction

Part 0 -- Introduction Professor William Greene; Economics and IOMS Departments Office: KMEC, 7-90 (Economics Department) Office phone: URL:

Part 0 -- Introduction 4

Part 0 -- Introduction Course Objectives Develop theoretical background for statistical analysis of data Develop tools used in regression analysis Tools for Estimation and Inference Linear regression model Nonlinear models, regression, probability

Part 0 -- Introduction Course Prerequisites Calculus – differential and integral Some matrix algebra (developed as needed during the course) Previous course in statistics up to simple (one variable) linear regression

Part 0 -- Introduction Course Materials Notes: Distributed in class (also via the course website). Text: Rice, J., Mathematical Statistics and Data Analysis, 3 rd Ed., Brooks/Cole Cengage, 2007 Optional Text: Greene, Econometric Analysis, Prentice Hall, (Chapters distributed in class.) Some computer work. Software provided in class.

Part 0 -- Introduction 8

Course Outline and Overview Mathematical Statistics Probabiity Distribution theory Estimation and Statistical Inference Regression Analysis Econometric modeling viewpoint Linear regression model Nonlinear regression and model building

Part 0 -- Introduction Agenda and Planning Guide (2/12) Probability theory, distributions, random variables (2/19) Limiting results: central limit theorem, law of large numbers (Homework 1) (2/26)Point and interval estimation, bayesian analysis (3/5)Normal family of distributions; estimation: moments, maximum likelihood (Homework 2) (3/12)Hypothesis testing: parametric, nonparametric (3/19) SPRING BREAK, NO CLASS (3/26)MIDTERM [Open book/notes; 30%] (Homework 3) (4/2Linear regression model – 1 (4/9)Linear regression model – 2 (Homework 4) (4/16)Linear regression model – 3 (4/23)Linear regression model – 4 (Homework 5) (4/30)Model building, nonlinear regression models (5/7)FINAL EXAM [Open book/notes; 50%] (Homework 6) Problem sets [20%; group work is permissible; submit one report]