1. Part 0: Introduction 0-1/17 Regression and Forecasting Models Professor William Greene Stern School of Business IOMS Department Department of Economics

2. Part 0: Introduction 0-2/17 Regression and Forecasting Models Part 0 - Introduction

3. Part 0: Introduction 0-3/17  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/regression/Outline.htm

4. Part 0: Introduction 0-4/17 Course Objectives  Basic understanding: The regression model as a framework for the analysis of relationships among variables  Technical know how: How to formulate a regression model, estimate its parameters, and understand the implications of the estimated model.

5. Part 0: Introduction 0-5/17 We used McDonald’s Per Capita

6. Part 0: Introduction 0-6/17 Macs and Movies Countries and Some of the Data Code Pop(mm) per cap # of Language Income McDonalds 1 Argentina 37 12090 173 Spanish 2 Chile, 15 9110 70 Spanish 3 Spain 39 19180 300 Spanish 4 Mexico 98 8810 270 Spanish 5 Germany 82 25010 1152 German 6 Austria 8 26310 159 German 7 Australia 19 25370 680 English 8 UK 60 23550 1152 UK Genres (MPAA) 1=Drama 2=Romance 3=Comedy 4=Action 5=Fantasy 6=Adventure 7=Family 8=Animated 9=Thriller 10=Mystery 11=Science Fiction 12=Horror 13=Crime

7. Part 0: Introduction 0-7/17 Movie Genres

8. Part 0: Introduction 0-8/17 Movie Madness Data (n=2198)

9. Part 0: Introduction 0-9/17

10. Part 0: Introduction 0-10/17 Case Study Using A Regression Model: A Huge Sports Contract  Alex Rodriguez hired by the Texas Rangers for something like $25 million per year in 2000.  Costs – the salary plus and minus some fine tuning of the numbers  Benefits – more fans in the stands.  How to determine if the benefits exceed the costs? Use a regression model.

11. Part 0: Introduction 0-11/17 Baseball Data (Panel Data – 31 Teams, 17 Years)

12. Part 0: Introduction 0-12/17 A Regression Model 

13. Part 0: Introduction 0-13/17  =.54914  1 = 11093.7  2 = 2201.2  3 = 14593.5

14. Part 0: Introduction 0-14/17 Marginal Value of an A Rod  8 games * 32,757 fans + 1 All Star = 35957 = 298,016 new fans  298,016 new fans * $18 per ticket $2.50 parking etc. $1.80 stuff (hats, bobble head dolls,…)  $6.67 Million per year !!!!!  It’s not close. (Marginal cost is at least $16.5M / year)

15. Part 0: Introduction 0-15/17 Course Prerequisites  Basic algebra. (Especially summation)  Geometry (straight lines)  Logs and exponents  NOTE: I (you) will use only base e (natural) logs, not base 10 (common) logs in this course.  Previous course in basic statistics – up to testing a hypothesis about a mean

16. Part 0: Introduction 0-16/17 Course Materials  Notes: Distributed in first class  Text: McClave, Benson, Sincich; Statistics for Business and Economics (2 nd Custom NYU edition), Pearson, 2011.  On the course website: Class slide presentations Problem sets Data sets for exercises http://people.stern.nyu.edu/wgreene/regression/Outline.htm

17. Part 0: Introduction 0-17/17 Course Software: Minitab The Current Version: Minitab 16 Buy: Professional Bookstore Rent: e5.onthehub.com $29.99 to rent for 6 months, $99.99 to own Search: e5.onthehub.com minitab