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Regression and Forecasting Models

Regression and Forecasting Models. Professor William Greene Stern School of Business IOMS Department Department of Economics. Regression and Forecasting Models. Part 0 - Introduction. Professor William Greene; Economics and IOMS Departments Office: KMEC, 7-90 (Economics Department)

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Regression and Forecasting Models

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  1. Regression and Forecasting Models Professor William Greene Stern School of Business IOMS Department Department of Economics

  2. Regression and Forecasting Models Part 0 - Introduction

  3. 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. 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. We used McDonald’s Per Capita

  6. Macs and Movies 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 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

  7. Movie Genres

  8. Movie Madness Data (n=2198)

  9. 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.

  10. Baseball Data (Panel Data – 31 Teams, 17 Years)

  11. A Regression Model 

  12.  = .54914 1 = 11093.7 2 = 2201.2 3 = 14593.5

  13. 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)

  14. 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

  15. Course Materials • Notes: Distributed in first class • Text: McClave, Benson, Sincich; Statistics for Business and Economics (2nd 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

  16. Course Software: MinitabThe 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

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