multicollinearity-sport

# multicollinearity-sport - 1 Multicollinearity Slide 2 3...

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Unformatted text preview: 1 Multicollinearity Slide # 2 3 Exam 3 Cumulative exam!! Includes 10 multiple choice questions from Exam 2 Slide # 4 Research Project Report Due Beginning of Class #14 Slide # 5 Research Project: Order of Testing A. Omitted variables and incorrect functional form (F, adj. R 2 , plots) Note: do either B. or C. but not both B. Serial correlation ( time series) C. Heteroskedasticity (cross section) D. Multicollinearity (corr. matrix, VIF) E. Irrelevant variables (t, joint significance) Slide # 6 II. Introduction Recall “Specification Error” OMIT possibly important variable OR INCLUDE possibly irrelevant variable Which one did you decide was the better choice? Slide # 7 Introduction (cont.) E. There might be consequences to adding variables to a model “Multicollinearity” is high correlation involving two or more IVs 1. One IV influences another IV 2. Three or more IVs influence each other Slide # 8 III. Multicollinearity Consequences 1. Can drastically alter results from one model to another 2. Can cause problems in interpreting results Slide # 9 Do Market Size and Wins Affect NBA Teams’ Profits? See regression output for multicollinearity. Profit Market Size Wins 33.4 3 57 22.0 3 63 16.0 3 46 8.7 2 42 5.4 2 44 4.7 2 55-1.5 1 35-2.1 1 13-4.0 1 28 NOTE: NBA market size = 3 for large, 2 for medium, 1 for small NOTE: Don't use this approach for measuring market size. Use a better measure like population. Profit is in millions of \$. Slide # 10 IV. Example Where PROFIT is annual team profit (\$1M) MKTSIZE = 3 for large, 2 for medium, 1 for small WINS is no. of wins in one season PROFIT = β 0 + β 1 MKTSIZE + β 2 WINS+ μ Slide # 11 Variables A B C Constant -17.16 -16.74 -17.06 (0.009) (0.10) (0.03) MKTSIZE 13.17 13.28 (0.0006) (0.02) WINS 0.61 -.008 (0.02) (0.98 ) Example (cont.) (p-values in parentheses) Slide # 12 Variables A B C Constant -17.16 -16.74 -17.06 (0.009) (0.10) (0.03) MKTSIZE 13.17 13.28 (0.0006) (0.02) WINS 0.61 -.008 (0.02) (0.98 ) (p-values in parentheses) Examine t-stat p-values Models A & B vs. model C Slide # 13 Variables A B C Constant -17.16 -16.74 -17.06 (0.009) (0.10) (0.03) MKTSIZE 13.17 13.28 (0.0006) (0.02) WINS 0.61 -.008 (0.02) (0.98 ) (p-values in parentheses) Examine coefficient values Models A & B vs. model C Slide # 14 Example (cont.) Point 1. Observed changes across models due to high correlation/multicollinearity between WINS & MKTSIZE correlation = 0.835 Slide # 15 Note 1. High correlation among IVs BAD 2. High correlation between IV, DV GOOD Slide # 16 V. Exact Multicollinearity A. Definition 1. Two or more IVs perfectly (or nearly perfectly) correlated 2. What would be values of correlation coefficients? Slide # 17 Exact Multicollinearity (cont.) B. Examples 1. POP, GDP in model explaining housing starts Correlation = 0.99 2. What determines profits?...
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multicollinearity-sport - 1 Multicollinearity Slide 2 3...

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