Develope a regression model to predict selling price based on square footage, nu
ID: 3377215 • Letter: D
Question
Develope a regression model to predict selling price based on square footage, number of bedrooms and age. Use this to predict the selling price of a 10-years old, 2,000 square foot with three bedrooms.
SELLING PRICE ($) SQUARE FOOTAGE BEDROOMS AGE (YEARS) 84000 1670 2 30 79000 1339 2 25 91500 1712 3 30 120000 1840 3 40 127500 2300 3 18 132500 2234 3 30 145000 2311 3 19 164000 2377 3 7 155000 2736 4 10 168000 2500 3 1 172500 2500 4 3 174000 2479 3 3 175000 2400 3 1 177500 3124 4 0 184000 2500 3 2 195500 4062 4 10 195000 2854 3 3Explanation / Answer
> ttt <- read.csv("clipboard",sep=" ") # data copied merely from the table given in the problem
> head(ttt)
SELLING.PRICE.... SQUARE.FOOTAGE BEDROOMS AGE..YEARS.
1 84000 1670 2 30
2 79000 1339 2 25
3 91500 1712 3 30
4 120000 1840 3 40
5 127500 2300 3 18
6 132500 2234 3 30
> names(ttt) <- c("Selling_Price","Sq_Foot","Bedrooms","Age")
> splm <- lm(Selling_Price~.,data=ttt)
> summary(splm)
Call:
lm(formula = Selling_Price ~ ., data = ttt)
Residuals:
Min 1Q Median 3Q Max
-19041 -11884 2456 7049 27455
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91446.49 26076.89 3.507 0.00386 **
Sq_Foot 29.86 10.86 2.749 0.01657 *
Bedrooms 2116.86 10003.01 0.212 0.83568
Age -1504.77 370.82 -4.058 0.00136 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15230 on 13 degrees of freedom
Multiple R-squared: 0.8678, Adjusted R-squared: 0.8373
F-statistic: 28.44 on 3 and 13 DF, p-value: 5.557e-06
> predict(splm,newdata=data.frame("Sq_Foot"=2000,"Bedrooms"=3,"Age"=10))
1
142465.2
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