* Condo Example
  See pages 447 to 465 of the Mendenhall and Sincich textbook.
  Examine 4 regression models for predicting sale prices
  of condo units at auction;

condo <- read.table("c:/datasets/condo.txt", header=T, sep="\t")
print(condo)

# car package is needed for anova function.
library(car)

model1 <- lm(PRICE100 ~ FLOOR + DIST + VIEW + END + FURNISH, 
   data=condo)
summary(model1)
anova(model1)

# Define full quadratic terms.
FLOORSQ <- condo$FLOOR * condo$FLOOR
DISTSQ <- condo$DIST * condo$DIST
FLR_DST <- condo$FLOOR * condo$DIST

model2 <- lm(PRICE100 ~ FLOOR + DIST + VIEW + END + FURNISH +
                FLOORSQ + DISTSQ + FLR_DST, data=condo)
summary(model2)
anova(model2)

# anova(model2, model1)

# Define interactions with VIEW
VIEW_FLOOR <- condo$VIEW * condo$FLOOR
VIEW_DIST <- condo$VIEW * condo$DIST
VIEW_FLOORSQ <- condo$VIEW * FLOORSQ
VIEW_DISTSQ <- condo$VIEW * DISTSQ
VIEW_FLR_DST <- condo$VIEW * FLR_DST

model3 <- lm(PRICE100 ~ FLOOR + DIST + VIEW + END + FURNISH +
                FLOORSQ + DISTSQ + FLR_DST + 
                VIEW_FLOOR + VIEW_DIST + VIEW_FLOORSQ +
                VIEW_DISTSQ + VIEW_FLR_DST, data=condo)
summary(model3)
anova(model3)

anova(model3, model2)

FLOOR_FACT <- as.factor(condo$FLOOR)

model4 <- lm(PRICE100 ~ FLOOR_FACT + DIST + DISTSQ + 
                FLOOR_FACT*DIST + VIEW + END + FURNISH +
                FLOOR_FACT*VIEW + VIEW_DIST + VIEW_DISTSQ + 
                FLOOR_FACT*DISTSQ + FLOOR_FACT*VIEW_DIST, 
                data=condo)
summary(model4)
anova(model4)

anova(model4, model3)