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

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

3. Part 0 -- Introduction Professor William Greene; Economics and IOMS Departments Office: KMEC, 7-90 (Economics Department) Office phone: 212-998-0876 Email: wgreene@stern.nyu.eduwgreene@stern.nyu.edu URL: http://people.stern.nyu.edu/wgreenehttp://people.stern.nyu.edu/wgreene http://people.stern.nyu.edu/wgreene/MathStat/Outline.htm

4. Part 0 -- Introduction 4 http://people.stern.nyu.edu/wgreene/MathStat/Outline.htm

5. 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

6. 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

7. 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, 2012. (Chapters distributed in class.) Some computer work. Software provided in class.

8. Part 0 -- Introduction 8

9. 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

10. 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]