# Call: lm(formula = price ~ waterfront, data = KCdataTrain)null Residuals: Min 1Q Median 3Q Max -1434491 -211684 -81684 108316 6358316null Coefficients: Estimate Std. LmWaterfront <- lm(price ~ waterfront, data = KCdataTrain) 'price', main = 'Train')Ībline(lmArea, col = 'blue', lwd = 2 )Qualitative predictors Plot(KCdataTrain$sqft_living, KCdataTrain$price, xlab = 'sqft_living', ylab = ' 0 ' ' 1null Residual standard error: 263300 on 17288 degrees of freedom Multiple R-squared: 0, Adjusted R-squared: 0. Plot(KCdata$yr_built, KCdata$price, xlab = 'yr_built', ylab = 'price') sqft_living 281 2 129 <2e-16 *** - Signif. Plot(KCdata$sqft_living, KCdata$price, xlab = 'sqft_living', ylab = 'price') Library(leaps) #Library for best subset selection Warning: package ' leaps' was built under R version 4.Read the data #install('leaps') #Run this line if the package leaps is not
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