Part 0 Introduction Statistical Inference and Regression Analysis

Part 0 -- Introduction Statistical Inference and Regression Analysis: Stat-GB. 3302. 30, UB. 0015. 01 Professor William Greene Stern School of Business IOMS 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: 212 -998 -0876 ¢ Email: wgreene@stern. nyu. edu ¢ URL: http: //people. stern. nyu. edu/wgreene/Math. Stat/Outline. htm

Part 0 -- Introduction http: //people. stern. nyu. edu/wgreene/Math. Stat/Outline. htm 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 l Linear Regression Model l

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: Hogg, Mc. Kean, Craig, 7 rd Ed. , Pearson, 2013 Optional Text: Greene, Econometric Analysis, Prentice Hall, 8 th Ed. , 2018. (Chapters distributed in class. ) Some computer work. Software provided in class.

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Part 0 -- Introduction Course Outline and Overview ¢ Mathematical Statistics l l l ¢ Probability Distribution theory Estimation and Statistical Inference Regression Analysis l l l Econometric modeling viewpoint Linear regression model Nonlinear regression and model building

Part 0 -- Introduction Agenda and Planning Guide ¢ ¢ ¢ ¢ (2/8) (2/15) (2/22) (3/1) (3/8) (3/15) (3/22) (3/29) (4/5) (4/12) (4/19) (4/26) (5/3) Probability theory, distributions, random variables Limiting results: central limit theorem, law of large numbers (Homework 1) Point and interval estimation, bayesian analysis Normal family of distributions; estimation: moments, maximum likelihood (Homework 2) Hypothesis testing: parametric, nonparametric SPRING BREAK, NO CLASS MIDTERM [Open book/notes; 40%] (Homework 3) Linear regression model – 1 Linear regression model – 2 (Homework 4) Linear regression model – 3 Linear regression model – 4 (Homework 5) Model building, nonlinear regression models FINAL EXAM [Open book/notes; 40%] (Homework 6) Problem sets [20%; group work is permissible; submit one report]