Question

Question 2:

Download the Excel data file "Arlington_Homes" from the folder "Data" under "Chapter 12."

a) read the data file in R.

b) using R, answer question 65 (a, b, and c) on page 411 of your book. Run the regression, show the estimates and test. Write what you are testing using a comment in the R program.

Question #65. link for page 411 #65 https://imgur.com/s0SgxP3

please show every step for R frmulas

Price	Sqft	Beds	Baths	Col
840000	2768	4	3.5	1
822000	2500	4	2.5	1
713000	2400	3	3	1
689000	2200	3	2.5	1
685000	2716	3	3.5	1
645000	2524	3	2	1
625000	2732	4	2.5	0
620000	2436	4	3.5	1
587500	2100	3	1.5	1
585000	1947	3	1.5	1
583000	2224	3	2.5	1
569000	3262	4	2	0
546000	1792	3	2	0
540000	1488	3	1.5	0
537000	2907	3	2.5	0
516000	1951	4	2	1
511000	1752	3	1.5	1
510000	1727	3	2	1
495000	1692	3	2	0
463000	1714	3	2	0
457000	1650	3	2	0
451000	1685	3	2	0
435000	1500	3	1.5	1
431700	1896	2	1.5	0
414000	1182	2	1.5	0
401500	1152	3	1	0
399000	1383	4	1	0
380000	1344	4	2	0
380000	1272	3	1	0
375900	2275	5	1	0
372000	1005	2	1	0
367500	1272	3	1	0
356500	1431	2	2	1
330000	1362	3	1	0
330000	1465	3	1	0
307500	850	1	1	0

Answer 1

Solutiona:

To read data:

library(readxl)
Arlington_Homes <- read_excel("C:/Users/M1045151/Downloads/Arlington_Homes.xlsx")
View(Arlington_Homes)

dim(Arlington_Homes)

glimpse(Arlington_Homes)

Output:

Observations: 36
Variables: 5
$ Price <dbl> 840000, 822000, 713000, 689000, 685000, 645000, 625000, 620000, 587500, 585000, 5...
$ Sqft <dbl> 2768, 2500, 2400, 2200, 2716, 2524, 2732, 2436, 2100, 1947, 2224, 3262, 1792, 148...
$ Beds <dbl> 4, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 4, ...
$ Baths <dbl> 3.5, 2.5, 3.0, 2.5, 3.5, 2.0, 2.5, 3.5, 1.5, 1.5, 2.5, 2.0, 2.0, 1.5, 2.5, 2.0, 1...
$ Col <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...
Solutionb:

Rcode to get the linear regression:

regmod <- lm(Arlington_Homes$Price ~Arlington_Homes$Sqft+Arlington_Homes$Beds+Arlington_Homes$Baths+Arlington_Homes$Col)
summary(regmod)

Call:

lm(formula = Arlington_Homes$Price ~ Arlington_Homes$Sqft + Arlington_Homes$Beds +

Arlington_Homes$Baths + Arlington_Homes$Col)

Residuals:

Min 1Q Median 3Q Max

-157118 -47479 -4742 38849 168327

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 165888.66 52353.54 3.169 0.00343 **

Arlington_Homes$Sqft 91.68 32.34 2.834 0.00801 **

Arlington_Homes$Beds 4372.36 18561.84 0.236 0.81533

Arlington_Homes$Baths 66619.61 24659.48 2.702 0.01109 *

Arlington_Homes$Col 74557.88 27374.26 2.724 0.01051 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 68440 on 31 degrees of freedom

Multiple R-squared: 0.777, Adjusted R-squared: 0.7483

F-statistic: 27.01 on 4 and 31 DF, p-value: 1.031e-09

Regression equation is

price=165888.66+91.68*sqft+4372.36 *Beds+ 66619.61*Baths+ 74557.88 *col

sqft,Beds,cola re significanct varaibles

F=27.01

p= 1.031e-09

p<0.05

Model is significant.