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Use the Regression Output Below to Answer the Following Questions (X)=( X ) =

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Use the regression output below to answer the following questions.
Lingar Reperaidion Anthyis: Dep Var (X)=( X ) = Weight
X={AX = \{ A Ee, Gender, Heifht, MBA, YY ear }\}
 Coefficients b Std.  Error  Std.  Beta t-test  Statistic p-value  Two Tailed  Intercept 210.60320.56010.2430.0000 Age 0.6600.2790.1012.3630.0186 Gender 17.4492.4500.2677.1220.0000 Height 4.9990.2940.61316.9820.0000 MBA 3.1223.0630.0431.0190.3087 Year 0.1110.5070.0060.2180.8274\begin{array}{||c|c|c|c|c|c|}\hline \text { Coefficients } & \boldsymbol{b} & \begin{array}{c}\text { Std. } \\\text { Error }\end{array} & \begin{array}{c}\text { Std. } \\\text { Beta }\end{array} & \begin{array}{c}\boldsymbol{t} \text {-test } \\\text { Statistic }\end{array} & \begin{array}{c}\boldsymbol{p} \text {-value } \\\text { Two Tailed }\end{array} \\\hline \text { Intercept } & -210.603 & 20.560 & & -10.243 & 0.0000 \\\hline \text { Age } & 0.660 & 0.279 & 0.101 & 2.363 & 0.0186 \\\hline \text { Gender } & 17.449 & 2.450 & 0.267 & 7.122 & 0.0000 \\\hline \text { Height } & 4.999 & 0.294 & 0.613 & 16.982 & 0.0000 \\\hline \text { MBA } & -3.122 & 3.063 & -0.043 & -1.019 & 0.3087 \\\hline \text { Year } & -0.111 & 0.507 & -0.006 & -0.218 & 0.8274 \\\hline\end{array}
rr2 Adj. r2SE(Reg)h0.8340.6960.69317.879448\begin{array}{|c|c|c|c|c|}\hline \boldsymbol{r} & \boldsymbol{r}^{2} & \text { Adj. } \boldsymbol{r}^{2} & \mathrm{SE}(\mathrm{Reg}) & \boldsymbol{h} \\\hline 0.834 & 0.696 & 0.693 & 17.879 & 448 \\\hline \hline\end{array}
 Source of  Variation  Sum of  Squares df Mean  Squares  F-test  Statistic p-value  One Tailed  Regression 323592.24564718.4202.4710.0000 Error 141282.23442319.643 Total 464874.47447\begin{array}{|c|c|c|c|c|c|}\hline \begin{array}{c}\text { Source of } \\\text { Variation }\end{array} & \begin{array}{c}\text { Sum of } \\\text { Squares }\end{array} & \boldsymbol{d f} & \begin{array}{c}\text { Mean } \\\text { Squares }\end{array} & \begin{array}{c}\text { F-test } \\\text { Statistic }\end{array} & \begin{array}{c}\boldsymbol{p} \text {-value } \\\text { One Tailed }\end{array} \\\hline \text { Regression } & 323592.24 & 5 & 64718.4 & 202.471 & 0.0000 \\\hline \text { Error } & 141282.23 & 442 & 319.643 & & \\\hline \text { Total } & 464874.47 & 447 & & & \\\hline\end{array}


-Given that Gender was coded female=0 and male=1,on the average,how much more do males weight than females,"correcting for" (i.e.,including the effects of)all the other variables in the regression?


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