Add Lecture 22 script.

lecture-scripts/lecture-22.R

@@ -0,0 +1,35 @@
+houses <- read.csv("https://raw.githubusercontent.com/brianlukoff/sta371g/master/data/houses.csv")
+
+.3*1728
+1:1728
+set.seed(1)
+test.cases <- sample(1:1728, 518)
+test.set <- houses[test.cases,]
+training.cases <- setdiff(1:1728, test.cases)
+training.set <- houses[training.cases,]
+
+abs(mean(training.set$Price) - mean(test.set$Price))
+
+model <- lm(Price ~ Living.Area, data=training.set)
+# mean absolute error (MAE) on the training set
+mean(abs(residuals(model)))
+# mean absolute error (MAE) on the test set
+predicted.prices <- predict(model, test.set)
+mean(abs(predicted.prices - test.set$Price))
+
+
+summary(lm(Price ~ Lot.Size + Waterfront + Land.Value + Fuel.Type + Living.Area, data=training.set))
+summary(lm(Price ~ Lot.Size + Waterfront + Land.Value + Living.Area, data=training.set))
+summary(lm(Price ~ Waterfront + Land.Value + Living.Area, data=training.set))
+summary(lm(Price ~ Land.Value + Living.Area, data=training.set))  # <====
+summary(lm(Price ~ Living.Area, data=training.set))
+
+model2 <- lm(Price ~ Land.Value + Living.Area, data=training.set)
+# MAE on the training set
+mean(abs(residuals(model2)))
+# MAE on the test set
+predicted.prices <- predict(model2, test.set)
+mean(abs(predicted.prices - test.set$Price))
+
+# R^2 on test set
+cor(predicted.prices, test.set$Price)^2