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Statistical Inference and Regression Analysis: Stat-GB.3302.30, Stat-UB.0015.01

Statistical Inference and Regression Analysis: Stat-GB.3302.30, Stat-UB.0015.01. Professor William Greene Stern School of Business IOMS Department Department of Economics. Statistical Inference and Regression Analysis. Part 0 - Introduction.  1/6.

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Statistical Inference and Regression Analysis: Stat-GB.3302.30, Stat-UB.0015.01

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

  2. Statistical Inference and Regression Analysis Part 0 - Introduction

  3.  1/6 • 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 http://people.stern.nyu.edu/wgreene/MathStat/Outline.htm

  4.  2/6 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

  5.  3/6 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

  6.  4/6 Course Materials • Notes: Distributed in class occasionally (via the course website). • Text: Rice, J., Mathematical Statistics and Data Analysis, 3rd Ed., Brooks/Cole Cengage, 2007 • Optional Text: Greene, Econometric Analysis, Prentice Hall, 2012. (Chapters distributed in class.) • Some computer work. Software provided in class.

  7.  5/6 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

  8.  6/6 Agenda and Planning Guide • (2/13) Probability theory, distributions, random variables • (2/20) Limiting results: central limit theorem, law of large numbers (Homework 1) • (2/27) Point and interval estimation, bayesian analysis • (3/6) Normal family of distributions; estimation: moments, maximum likelihood (Homework 2) • (3/13) Hypothesis testing: parametric, nonparametric • (3/20) SPRING BREAK, NO CLASS • (3/27) MIDTERM [Open book/notes; 30%] (Homework 3) • (4/3) Linear regression model – 1 • (4/10) Linear regression model – 2 (Homework 4) • (4/17) Linear regression model – 3 • (4/24) Linear regression model – 4 (Homework 5) • (5/1) Model building, nonlinear regression models • (5/8) FINAL EXAM [Open book/notes; 50%] (Homework 6) Problem sets [20%; group work is permissible; submit one report]

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