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Data Was Collected from 40 Employees to Develop a Regression

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Data was collected from 40 employees to develop a regression model to predict the employee's annual salary using their years with the company (Years),their starting salary (Starting),and their Gender (Male = 0,Female = 1).The results from Excel regression analysis are shown below:
 RegresionSlalinlics  Multiple R  0.719714957  R Square  0.516551199  Adjusted R Square 0.476253780 Standard Errar 10515.63461 Dbservations 40\begin{array}{l}\begin{array} { l r } \hline { \text { RegresionSlalinlics } } \\\hline \text { Multiple R } & \text { 0.719714957 } \\\text { R Square } & \text { 0.516551199 } \\\text { Adjusted R Square } & 0.476253780 \\\text { Standard Errar } & 10515.63461 \\\text { Dbservations } & 40 \\\hline\end{array}\\\end{array}

 ANOVA \text { ANOVA }
 of  SS  MS F Significance F Regression 34334682510144489417012.821655857.48476E06 Residual 364056901131112691698.1 Total 398391583641\begin{array}{lrrrrrr}\hline & \text { of } & & \text { SS } & \text { MS } & F & \text { Significance } F \\\hline \text { Regression } & 3 & 4334682510 & 1444894170 & 12.82165585 & 7.48476 \mathrm{E}-06 \\\text { Residual } & 36 & 4056901131 & 112691698.1 & & \\\text { Total } & 39 & 8391583641 & & & \\\hline\end{array}

 Coefficients  Standard Error t Stat P-value  Intercept 27946.578944832.4387065.7831212451.35464E06 Years 1665.251558425.08290923.9174747370.000383313 Starting 0.2663741850.126104432.1123301120.041661598 Gender 3285.5410435617.1453920.5849129430.56225464\begin{array}{lcrrr}\hline & \text { Coefficients } &{\text { Standard Error }} & {t \text { Stat }} &{P \text {-value }} \\\hline \text { Intercept } & 27946.57894 & 4832.438706 & 5.783121245 & 1.35464 \mathrm{E}-06 \\\text { Years } & 1665.251558 & 425.0829092 & 3.917474737 & 0.000383313 \\\text { Starting } & 0.266374185 & 0.12610443 & 2.112330112 & 0.041661598 \\\text { Gender } & -3285.541043 & 5617.145392 & -0.584912943 & 0.56225464\end{array}


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