# Montgomery, Exercise 9.10
library(MPV) # contains the dataset table.b1
library(car) # for vif

lm.fit <- lm(y ~ ., data = table.b4)
vif(lm.fit)

summ <- summary(lm.fit)

X1 <- model.matrix(lm.fit) # get the X matrix
X <- X1[, -1] # remove the intercept column (column 1)
WtW <- cor(X)
WtW

# VIF
vif(lm.fit)
# or diagonals in
solve(WtW)

# Condition number
eval <- eigen(WtW)
eval
condition_number <- sqrt(max(eval$values) / min(eval$values))
condition_number # (<100 so no serious problem see p. 298 Montgomery)