3. Find the linear correlation coefficient, r. Round to four decimal places. 4. Find R2 and interpret it in context. Round to the hundredth percent.
3. Find the linear correlation coefficient, r. Round to four decimal places. 4. Find R2 and interpret it in context. Round to the hundredth percent.
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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![3. Find the linear correlation coefficient, r. Round to four decimal places.
4. Find R2 and interpret it in context. Round to the hundredth percent.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F35038f16-94a1-4399-bb46-35e628254d59%2F0856d50b-ea6b-49ed-b2fe-cec590885017%2Fnll5ylo_processed.jpeg&w=3840&q=75)
Transcribed Image Text:3. Find the linear correlation coefficient, r. Round to four decimal places.
4. Find R2 and interpret it in context. Round to the hundredth percent.
![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
T
1400
1600
square Footage
cor(price,footage)
## [1] 0.7088847
pricemodel<-lm(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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F35038f16-94a1-4399-bb46-35e628254d59%2F0856d50b-ea6b-49ed-b2fe-cec590885017%2Fsqp08ji_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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
T
1400
1600
square Footage
cor(price,footage)
## [1] 0.7088847
pricemodel<-lm(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
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