Question

Estimate: Price = β₀ + β₁Sqft + β₂Beds + β₂Baths + ε, where Price, Sqft, Beds, and Baths refer to home price, square footage, number of bedrooms, and number of bathrooms, respectively.

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

a. Use R’s predict() function to construct the 95% confidence interval for the expected price of a 2,500-square-foot home in Arlington, Massachusetts, with three bedrooms and two bathrooms. (Round answers to 2 decimal places.)

Confidence interval:

b. Use R’s predict() function to construct the corresponding prediction interval for an individual home. (Round answers to 2 decimal places.)

Prediction interval:

Answer 1

data = read.table("../chandan/Documents/Tutoring/random data /price",header=T)
model <- lm(Price~Sqft+Beds+Baths,data = data)
summary(model)
predict(model,data.frame(Sqft=2500,Beds=3,Baths=2),interval="confidence")

fit      lwr      upr
1 578704.6 528486.5 628922.7

a)

95% confidence interval is

(528486.5,628922.7)

b)

for prediction interval

predict(model,data.frame(Sqft=2500,Beds=3,Baths=2),interval="prediction")
       fit      lwr      upr
1 578704.6 417921.6 739487.7

95% prediction interval is

(417921.6,739487.7)