Question 2: Download the Excel data file \"Arlington_Homes\" from the folder \"D
ID: 2947237 • Letter: Q
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
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
Explanation / Answer
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.
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