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

question 21

Essay

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


-Based on the regression printout,what would be the predicted (mean)weight of a female,20 years old,68 inches tall,without an MBA,and in Year 1.

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Definitions:

Pearson Correlation

A measure of the linear correlation between two variables X and Y, giving a value between +1 and -1 inclusive, where 1 is total positive linear correlation, 0 is no linear correlation, and -1 is total negative linear correlation.

Critical Values

Thresholds determined by the significance level of a test, marking the boundary within which the null hypothesis is accepted or rejected.

Pearson Correlation

A measure of the linear relationship between two continuous variables, ranging from -1 to 1, where 1 means a perfect positive correlation.

Pearson Correlation

Another name for Pearson r, indicating the degree of linear relationship between two variables.