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Use data from HPRICE_1DTA and the Breusch Pagan Test for conditionally heteroskedastic errirs in the regressions model.

Price = B0 + B1Bdrms+B2Sqrft+U

Give the value of the test statistic

Use F-test version

Please show your work. Thank you!

regress price bdrms sarft, robust Linear regression Number of obs F(2, 69) Prob > F R-squared Root MSE 72 29.55 0.0000 0.6618 56.228 Robust Std. Err. price Coef. t P>|t| (95% Conf. Interval] bdrms sarft cons 21.00007 .1152072 -17.25434 8.796846 .0167099 40.10067 2.39 6.89 -0.43 0.020 0.000 0.668 3.450847 .081872 -97.25299 38.5493 .1485425 62.74431 predict resid, residuals gen residsq = resid^2 regress residsq bdrms sarft Source SS df MS 72 = 4.84 = Model Residual 455633843 3.2504e+09 2 69 227816922 47107747.4 Number of obs F(2, 69) Prob > F R-squared Adj R-squared Root MSE = 0.0108 0.1229 0.0975 6863.5 Total 3.7061e+09 71 52198146.7 residsa Coef. Std. Err. t P>It! [95% Conf. Interval] bdrms sqrft _cons 1097.839 3.315872 -7733.813 1126.483 1.644671 3789.974 0.97 2.02 -2.04 0.333 0.048 0.045 -1149.433 .0348425 -15294.6 3345.11 6.596901 -173.0207 regress price bdrms sarft, robust Linear regression Number of obs F(2, 69) Prob > F R-squared Root MSE 72 29.55 0.0000 0.6618 56.228 Robust Std. Err. price Coef. t P>|t| (95% Conf. Interval] bdrms sarft cons 21.00007 .1152072 -17.25434 8.796846 .0167099 40.10067 2.39 6.89 -0.43 0.020 0.000 0.668 3.450847 .081872 -97.25299 38.5493 .1485425 62.74431 predict resid, residuals gen residsq = resid^2 regress residsq bdrms sarft Source SS df MS 72 = 4.84 = Model Residual 455633843 3.2504e+09 2 69 227816922 47107747.4 Number of obs F(2, 69) Prob > F R-squared Adj R-squared Root MSE = 0.0108 0.1229 0.0975 6863.5 Total 3.7061e+09 71 52198146.7 residsa Coef. Std. Err. t P>It! [95% Conf. Interval] bdrms sqrft _cons 1097.839 3.315872 -7733.813 1126.483 1.644671 3789.974 0.97 2.02 -2.04 0.333 0.048 0.045 -1149.433 .0348425 -15294.6 3345.11 6.596901 -173.0207