HW6 - ORIE 4740

# HW6 - ORIE 4740 - HW#(sj259 1 .(logSalary,...

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HW # 6 – ORIE 4740                                      Shradha  Jain  (sj259) 1) Run the regression on full model below. Log transformed salary (logSalary), the outcome variable before.  baseballFull <- lm( formula = logSalary~bat+obp+runs+hits+doubles+  triples+hr+rbi+walks+so+sb+errors+free.elig+free.91.2+arb.elig+arb.91.2,  data = baseball )         summary(baseballFull)         p-values             runs - 0.80381   |   hits  - 0.05439 So, running regression on the full model, the p-value for runs was 0.8038 and for hits, it was 0.05439. # remove runs baseballFull <- lm( formula = logSalary~bat+obp+hits+doubles+  triples+hr+rbi+walks+so+sb+errors+free.elig+free.91.2+arb.elig+arb.91.2,  data = baseball ) summary(baseballFull) After removing runs, the p-value of hits changed from 0.05439 to 0.018056. The pairwise scatterplots  below show that hits and runs were highly correlated, so after we removed runs, the p-value for hits  decreased and became more significant.

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runs 0 50 100 150 200 0 20 40 60 80 100 0 20 40 60 80 100 50 150 200 hits 1 continued)
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## This note was uploaded on 12/13/2009 for the course ORIE 4740 at Cornell.

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HW6 - ORIE 4740 - HW#(sj259 1 .(logSalary,...

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