Can you help me solve iii,iv, and v using R? Here is what I have so far. 2. The data below contains sale price, size, and land-to-building ratio for 10 large industrial properties ```{r}saleprice <- read.csv("https://www.siue.edu/~jpailde/saleprice.csv")saleprice``` i) Construct a scatterpot for `sale price versus size` and `sale price versus land-to-building ratio`. Be sure to fit regression lines on the scatterplots. ii) Use the `lm` function to estimated the equations of each regression model for `sale price versus size` and `sale price versus land-to-building ratio`. iii) Check the error model assumption visually by constructing a residual plot and QQplot of the residuals for the two models. iv) Estimate the population regression slope of each model (line) by constructing 95\% confidence interval. Give a brief interpretation of the esimated slope in the context of the problem. v) Perform a hypothesis test on the regression slope of each model (line), use a 5\% level of significance. Given an appropriate conclusion. ### Code chunk```{r} # start your code#i and iicolnames(saleprice)<- c("Property","Size","Sale_Price", "Land_Building_Ratio")colnames(saleprice) ggplot(saleprice)+ aes(x=Size, y=Sale_Price)+ geom_point()+ labs(title = "Sale Price vs Size", x= "Size", y= "Sale_Price")+ geom_smooth(method = lm) ggplot(saleprice, aes(x=Land_Building_Ratio, y=Sale_Price))+ geom_point()+ labs(title = "Sale Price vs Land to Building Ratio", x="Land to Building Ratio", y="Sale Price (millions of dollars)")+ geom_smooth(method = lm) #iimodel1<- lm(Sale_Price~Size,data=saleprice)model2<- lm(Sale_Price~Land_Building_Ratio, data = saleprice) summary(model1)summary(model2) confint(model1)confint(model2) coeftest(model1)coeftest(model2) # last R code line
Can you help me solve iii,iv, and v using R?
Here is what I have so far.
2. The data below contains sale price, size, and land-to-building ratio for 10 large industrial properties
```{r}
saleprice <- read.csv("https://www.siue.edu/~jpailde/saleprice.csv")
saleprice
```
i) Construct a scatterpot for `sale price versus size` and `sale price versus land-to-building ratio`. Be sure to fit regression lines on the
ii) Use the `lm`
iii) Check the error model assumption visually by constructing a residual plot and QQplot of the residuals for the two models.
iv) Estimate the population regression slope of each model (line) by constructing 95\% confidence interval. Give a brief interpretation of the esimated slope in the context of the problem.
v) Perform a hypothesis test on the regression slope of each model (line), use a 5\% level of significance. Given an appropriate conclusion.
### Code chunk
```{r}
# start your code
#i and ii
colnames(saleprice)<- c("Property","Size","Sale_Price", "Land_Building_Ratio")
colnames(saleprice)
ggplot(saleprice)+
aes(x=Size, y=Sale_Price)+
geom_point()+
labs(title = "Sale Price vs Size", x= "Size", y= "Sale_Price")+
geom_smooth(method = lm)
ggplot(saleprice, aes(x=Land_Building_Ratio, y=Sale_Price))+
geom_point()+
labs(title = "Sale Price vs Land to Building Ratio", x="Land to Building Ratio", y="Sale Price (millions of dollars)")+
geom_smooth(method = lm)
#ii
model1<- lm(Sale_Price~Size,data=saleprice)
model2<- lm(Sale_Price~Land_Building_Ratio, data = saleprice)
summary(model1)
summary(model2)
confint(model1)
confint(model2)
coeftest(model1)
coeftest(model2)
# last R code line
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