ch8_1way_part1_4pp

# The regression model with dummy variables

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Unformatted text preview: group [1] P F P C C P F F P F C C C F F P C P C C F F C P P [26] F F C F P C C P P P C C C F F F P P F P Levels: C F P > is.factor(group) [1] TRUE ## See how R has coded the dummy variables for ## the ‘factor’ variable group using contrasts(): > contrasts(group) FP C00 F10 P01 So, C is the baseline group (because both dummy variables are 0). 11 12 What do we get when we ﬁt the model in R. The regression model with dummy variables: > is.factor(group) [1] TRUE Yi = β0 + βF D1i + βP D2i + ￿i > lm.out=lm(rate ~ group) The model for each group (3 separate means): > coefficients(lm.out) (Intercept) groupF 82.524067 8.801067 Yi = β0 + ￿i Yi = (β0 + βF ) + ￿i Yi = (β0 + βP ) + ￿i Control group: Friend group: Pet group: ˆ β0 ˆ βF ˆ βP The group means: > tapply(rate,group,mean) C F P 82.52407 91.32513 73.48307 0.20 100 0.25 0.30 0.35 The expected value of Y is the same for all i within a group. groupP -9.041000 0.00 0.05 90 0.10 0.15 ● 5 10 15 20 25 80 0 Y 60 70 Written another way... E [Yi|groupi = C ] = µC = β0 E [Yi|groupi = F ] = µF = β0 + βF E [Yi|groupi = P ] = µP = β0 + βP C F P The distributions: 13 14 The dummy variable parameters represent t...
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## This note was uploaded on 06/12/2013 for the course MATHEMATIC MAT7870 taught by Professor Sun during the Winter '13 term at Wayne State University.

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