Write the equation of the least-squares regression line defining any variables used. Round coefficients to4 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"))600500400300T14001600square Footagecor(price,footage)## [1] 0.7088847pricemodel<-lm(price-footage)msummary(pricemodel)18002000##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

From the given data we have , Intercept=21.14411 coefficient of footage=0.28388

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