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Nov 24, 2024

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MGEC11, Assignment 2 Suggested Solutions Yue Yu Fall 2023 Q1 (i) library (wooldridge) data ( hprice1 ) names (hprice1) ## [1] "price" "assess" "bdrms" "lotsize" "sqrft" "colonial" ## [7] "lprice" "lassess" "llotsize" "lsqrft" (ii) q1.ols1 <- lm (price ~ sqrft + bdrms, data= hprice1) summary ( q1.ols1 ) ## ## Call: ## lm(formula = price ~ sqrft + bdrms, data = hprice1) ## ## Residuals: ## Min 1Q Median 3Q Max ## -127.627 -42.876 -7.051 32.589 229.003 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -19.31500 31.04662 -0.622 0.536 ## sqrft 0.12844 0.01382 9.291 1.39e-14 *** ## bdrms 15.19819 9.48352 1.603 0.113 ## --- ## Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 ## ## Residual standard error: 63.04 on 85 degrees of freedom ## Multiple R-squared: 0.6319, Adjusted R-squared: 0.6233 ## F-statistic: 72.96 on 2 and 85 DF, p-value: < 2.2e-16 (iii) Holding square footage constant, the estimated increase in price for a house with two more bedrooms is $30,396 (=15.198x2x1000). Note that the unit of price is thousand dollars. 1
(iv) The estimated increase in price for a house with an additional 50 square footage is $6,422, (=0.12844x50x1000), holding the number of bedrooms constant. (v) Based on R-squared, 63% of the variation in price is explained by square footage and number of bedrooms. (vi) phat = q1.ols1 $ coefficients[ 1 ] + q1.ols1 $ coefficients[ 2 ] * 2076 + q1.ols1 $ coefficients[ 3 ] * 3 phat ## (Intercept) ## 292.9131 The predicted selling price is $292,913. (vii) The residual for this house is $77,087. Because the predicted price is lower than the actual price, it suggests that the buyer overpaid for the house. presi = 370 - phat presi ## (Intercept) ## 77.08685 2
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