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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

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

MATLAB: An Introduction with Applications
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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Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places.
Transcribed Image Text:Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places.
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
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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