pdf("lacrosse.pdf") lacrosse1 <- read.fwf("C:\\data\\lacrosse.dat", width=c(8,8,14), col.names=c("brand", "side", "gadd")) attach(lacrosse1) brand <- factor(brand, levels=1:4, labels=c("SHC", "SCHAF", "SCHUL", "BUL")) side <- factor(side, levels=1:2, labels=c("Front", "Back")) tapply(gadd, brand, mean) # marginal mean for brand tapply(gadd, side, mean) # marginal mean for side tapply(gadd, list(brand,side), mean) # cell means tapply(gadd, list(brand,side), sd) # cell SDs # Fit Model with Interaction (A*B says to include main effects and interactions) lacrosse1.aov <- aov(gadd ~ brand*side) summary(lacrosse1.aov) # Plot Means of Front and Back versus Brand interaction.plot(brand,side,gadd) # Plot residuals versus predicted values yhat <- predict(lacrosse1.aov) e <- resid(lacrosse1.aov) plot(yhat,e) dev.off()