# Create and analyze simple regression model 
# for predicting weight from height for players
# on Chicago Bears 2024 Roster.

# Read dataframe from file.
bears_df = read.csv("bears-2024-roster.txt", header=T)

# Convert height to meters.
h <- (bears_df$HtFt + bears_df$HtIn / 12) * 0.3048

# Convert weight to kilos.
w <- bears_df$WtLb * 0.4536

# Create scatterplot of weight vs. height
png("scatterplot.png", 500, 500, units="px")
plot(h, w, xlab="Height in Meters", ylab="Weight in Kilos", 
    main="Bears 2024 Roster")
dev.off( )

# Obtain regression equation for predicting 
# weight from height.
df <- data.frame(Weight=w, Height=h)
model <- lm(Weight ~ Height, df)

# Print regression model and summary
print(model)
print(summary(model))

# Residual Plot
png("residual-plot.png", 500, 500, units="px")
plot(predict(model), resid(model), xlab="Predicted Values",
    ylab="Residuals", main="Residual Plot")
dev.off( )

# Box Plot of Residuals
png("boxplot.png", 500, 500, units="px")
boxplot(resid(model), main="Boxplot of Residuals")
dev.off( )

# Normal Plot of Residuals
png("normal-plot.png", 500, 500, units="px")
qqnorm(resid(model), main="Normal Plot of Residuals")
dev.off( )