Credit <- read.csv("../data/Credit.csv")
Credit = Credit[,-1]    #Removing column that just numbers the observations
par(mfrow=c(1,3))
boxplot(Balance~Gender,data=Credit)
boxplot(Balance~Ethnicity,data=Credit)
boxplot(Balance~Student,data=Credit)

class(Credit$Gender)
class(Credit$Student)
class(Credit$Ethnicity)
class(Credit$Gender) = is.factor(Credit$Gender)
Credit$Gen2 = as.integer(Credit$Gender=="Female")
fit = lm(Balance~Gender,data=Credit)
summary(fit)
fit2 = lm(Balance~Gen2,data=Credit)
summary(fit2)

class(Credit$Student) = is.factor(Credit$Student)
class(Credit$Ethnicity) = is.factor(Credit$Ethnicity)

class(Credit$Gen2) = is.factor(Credit$Gen2)
fit.lm <- lm(Balance~Student+Ethnicity,data=Credit)
summary(fit.lm)

fit2.lm <- lm(Balance~Student,data=Credit)
summary(fit2.lm)

fit5.lm <- lm(Balance~Limit,data=Credit)
summary(fit5.lm)

fit6.lm <- lm(Balance~Rating,data=Credit)
summary(fit6.lm)

fit7.lm <- lm(Balance~Limit+Rating,data=Credit)
summary(fit7.lm)


fit.lm2 <- lm(Balance~Age+Cards+Education+Income+Limit+Rating+Student+Ethnicity,data=Credit)
summary(fit.lm2)

par(mfrow=c(1,2))
fit.lm3 <- lm(Balance~Income+Student,data=Credit)
beta <- coef(fit.lm3)
x <- seq(0,150,by=1)
matplot(cbind(x,x),cbind(beta[1]+beta[2]*x,beta[1]+beta[2]*x+beta[3]),col=c(1,2),
        type="l",lty=1,xlab="Income",ylab="Balance")
legend("topleft",lty=1,col=1:2,legend=c("student","non-student"))

fit.lm4 <- lm(Balance~Income+Student+Income:Student,data=Credit)
beta <- coef(fit.lm4)
x <- seq(0,150,by=1)
matplot(cbind(x,x),cbind(beta[1]+beta[2]*x,beta[1]+beta[2]*x+beta[3]+beta[4]*x),col=c(1,2),
        type="l",lty=1,xlab="Income",ylab="Balance")
legend("topleft",lty=1,col=1:2,legend=c("student","non-student"))

fit.lm <- lm(Balance~Limit+Rating,data=Credit)
summary(fit.lm)

fit.poly1 = lm(Balance~Limit,data=Credit)
fit.poly = lm(Balance~poly(Limit,3),data=Credit)