5. Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places .
Extracted text: footage<-c(1561,1818,1421,1488,1356,1430,1881,1253,1300,1500,1806,1777,1995)><-c(425,489,379,499,299,380,560,429,524.9,429.9,580,459,665) xyplot(price-footage,="" xlab="" "square="" footage",="" ylab="Asking price($000)" ,="" type="">
![600<br>500<br>400<br>300<br>1400<br>1600<br>1800<br>2000<br>square Footage<br>cor(price, footage)<br>## [1] 0.7088847<br>pricemodel<-1m(price~footage)<br>msummary(pricemodel)<br>Estimate Std. Error t value Pr(> |t|)<br>##<br>## (Intercept)<br>## footage<br>21.14411 136.31449<br>0.155 0.87954<br>0.28388<br>0.08516 3.333 0.00667 **<br>#2#<br>## Residual standard error: 71.64 on 11 degrees of freedom<br>## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573<br>## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671<br>Asking price($000)<br>](https://s3.us-east-1.amazonaws.com/storage.unifolks.com/qimg-008/008_dovyzj0-3hgalsik.jpeg)
Extracted text: 600 500 400 300 1400 1600 1800 2000 square Footage cor(price, footage) ## [1] 0.7088847 pricemodel<-1m(price~footage) msummary(pricemodel)="" estimate="" std.="" error="" t="" value="" pr(=""> |t|) ## ## (Intercept) ## footage 21.14411 136.31449 0.155 0.87954 0.28388 0.08516 3.333 0.00667 ** #2# ## 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 Asking price($000)