###---------------------------------------------------------------------------- ### 8.26 Multiple Regression ###---------------------------------------------------------------------------- ## Read the data CableData <- read.table("Table_8_10.csv", header = TRUE) ## Make a linear regression CableLM <- lm(Y~X2+X3+X4+X5+X6, data = CableData) # With intercept CableLMNoInt <- lm(Y~0+X2+X3+X4+X5+X6, data = CableData) # Without intercept ## Summarize the models summary(CableLM) summary(CableLMNoInt) ## See what are available in the regression object names(CableLM) CableLM\$fitted.values # Y Hat CableLM\$residuals # Residuals ## Predictions predict(CableLM, se.fit = TRUE) #same as CableLM\$fitted.values but also show #the standard error for the predictions ## The coefficients coef(CableLM) ## Variance covariance matrix for coefficients vcov(CableLM) ## Prediction with new values predict(CableLM, newdata = CableData[1:6, ], se.fit = TRUE) ## Plot the model plot(CableLM) ## Histogram of the residuals hist(CableLM\$residuals) ###---------------------------------------------------------------------------- ### 9.22 Regression with dummies ###---------------------------------------------------------------------------- ## Read the data SalesData <- read.table("Table_9_3.csv", header = TRUE) ## Create dummies, ## Seasonal data, n <- nrow(SalesData) D2 <- as.numeric(((1:n)%%4) == 2) # Check if from quarter II D3 <- as.numeric(((1:n)%%4) == 3) # Check if from quarter III D4 <- as.numeric(((1:n)%%4) == 0) # Check if from quarter IV SalesDataWithDummies <- cbind(SalesData, D2, D3, D4) DISHLM <- lm(DISH~D2+D3+D4, data = SalesDataWithDummies) FRIGLM <- lm(FRIG~D2+D3+D4, data = SalesDataWithDummies) summary(DISHLM) summary(FRIGLM) ## Regression with dummies is exactly the same as the usual regression