Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footage<- c(1561,1818,1421,1488,1356,1430,1881,1253,1300,1500,1806, 1777,1995) price<- c(425,489,379,499,299,380,560,429,524.9,429.9,580,459,665) xyplot(price-footage, Asking Price ($000) xlab "square Footage", ylab "Asking Price($000)", type=c("p","r")) 600- 500 400- 300 1400 cor(price,footage) ## [1] 0.7088847 1600 square Footage pricemodel<-Im(price-footage) msummary(pricemodel) 1800 2000 ## Estimate Std. Error t value Pr(>t) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footage<- c(1561,1818,1421,1488,1356,1430,1881,1253,1300,1500,1806, 1777,1995) price<- c(425,489,379,499,299,380,560,429,524.9,429.9,580,459,665) xyplot(price-footage, Asking Price ($000) xlab "square Footage", ylab "Asking Price($000)", type=c("p","r")) 600- 500 400- 300 1400 cor(price,footage) ## [1] 0.7088847 1600 square Footage pricemodel<-Im(price-footage) msummary(pricemodel) 1800 2000 ## Estimate Std. Error t value Pr(>t) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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![Is it reasonable to interpret the y-intercept of the regression line? Why or why not?
One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average
for a home of this size? Use residuals and show your work to answer.
footage<-
c(1561,1818,1421,1488,1356,1430,1881,1253,1300,1500,1806,
1777,1995)
price<-
c(425,489,379,499,299,380,560,429,524.9,429.9,580,459,665)
xyplot(price-footage,
Asking Price($000)
xlab "square Footage",
ylab "Asking Price($000)",
type=c("p","r"))
600
500
400
300
1400
1600
square Footage
1800
O
2000
cor(price,footage)
## [1] 0.7088847
pricemodel<-Im(price-footage)
msummary(pricemodel)
##
Estimate Std. Error t value Pr(>t)
## (Intercept) 21.14411 136.31449 0.155 0.87954
## footage 0.28388 0.08516 3.333 0.00667 **
## Residual standard error: 71.64 on 11 degrees of freedom
## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573
## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F35038f16-94a1-4399-bb46-35e628254d59%2Ff667eba1-b698-44b3-ac47-a8197c1e85bd%2Fa7w0xdb_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Is it reasonable to interpret the y-intercept of the regression line? Why or why not?
One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average
for a home of this size? Use residuals and show your work to answer.
footage<-
c(1561,1818,1421,1488,1356,1430,1881,1253,1300,1500,1806,
1777,1995)
price<-
c(425,489,379,499,299,380,560,429,524.9,429.9,580,459,665)
xyplot(price-footage,
Asking Price($000)
xlab "square Footage",
ylab "Asking Price($000)",
type=c("p","r"))
600
500
400
300
1400
1600
square Footage
1800
O
2000
cor(price,footage)
## [1] 0.7088847
pricemodel<-Im(price-footage)
msummary(pricemodel)
##
Estimate Std. Error t value Pr(>t)
## (Intercept) 21.14411 136.31449 0.155 0.87954
## footage 0.28388 0.08516 3.333 0.00667 **
## Residual standard error: 71.64 on 11 degrees of freedom
## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573
## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
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