```{r} install.packages(“ISLR”) library(ISLR) attach(Carseats) head(Carseats) summary(Carseats)
#PART A
```{r}
fit<-lm(Sales~Price + Urban + US, data = Carseats)
summary(fit)#part b The coefficienet for price is -0.054459 which means taht for every dollar increase in the price of my career, my store;s sales decrease by &54 on average.
The coefficient for US =yES IS 1.200573 Which means our carseats sell, on average US Stores carseats selll $1200 more compared to stores outside the US
#part c:
{r} Sales = 13.04 -0.05 Price -0.22 UrbanYes + 1.2 USYes
how well v Terrible, adjusted R squared is 0.2335 for part a and adjusted R squared is 0.2354 for part e . I would prefer adjusted R squared >0.7
{r} confint(fit)
```{r} par(mfrow=c(1,1)) plot(fit)
```{r}
summary(influence.measures(fit))