Question

SellingPrice	Age	Miles
13558	5	61476
13764	8	54397
22905	1	8281
15280	1	24871
16430	2	22139
16645	2	23741
16992	2	47426
18444	2	16865
18849	3	35394
19877	3	29634
11812	6	55785
14901	1	46212
15903	3	37007
16502	5	45473
9415	10	86861
12931	6	77208
15731	9	59603
10536	7	93212
8907	8	48263
11996	6	42434

A) Determine the sample regression equation that enables us to predict the price of a sedan on the basis of its age and mileage. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) [If you are using R to obtain the output, then first enter the following command at the prompt: options(scipen=10). This will ensure that the output is not in scientific notation.]

B) Use the predict() function in R or use the regression output to predict the selling price of a six-year-old sedan with 63,000 miles. (Round answer to 2 decimal places.)

Answer 1

Solution:

A)

R Code

df<-read.csv(file.choose()) # Choose the csv file which contains the above data
reg_model<-lm(df$SellingPrice~df$Age+df$Miles, data = df) # Regression Line
reg_model

Output:

So the regression line becomes

B)

R code to predict the Sell Price

predict(reg_model,data.frame(Age=6,Miles=63000)) # Predict Sell price for age =6 and miles=63000

Therefore, the predicted sell Price for the 6-year-old sedan with 63,000 miles is 13089.10

Complete R code

df<-read.csv(file.choose())
attach(df)
reg_model<-lm(SellingPrice~Age+Miles, data = df)
reg_model

predict(reg_model,data.frame(Age=6,Miles=63000))