Now its time assess the following: whether there is any redundancy between the two selection procedure variables (i.e., is there a very large correlation between the two?), if both are still significantly associated with job performance when specified in the same regression model (p-value), and what the collective criterion-related validity of the selection procedures are in terms of the R-squared value. Once you have run the analyses, respond to the following questions: 1. Is there any concerning amount of redundancy between scores on the structured interview and scores on the work sample? How do you know? Be specific and use numbers to support your answers. 2. Are both selection procedure variables still significantly associated with job performance when specified in the same multiple linear regression model? How do you know? 3. What is the R-squared value for the model? What does this value indicate? 4. Do you recommend that the organization use both selection procedures in the future? Why or why not? 5. Describe what you did to a manager. Remember that the manager is not well- versed in data analytics, so you have to use lay terms to help them understand your procedure, results, and what they mean.

AutoSave Chapter 7 - Data Set HW - Search File Home Insert Draw Page Layout Formulas Data Review View Help (i) PRODUCT NOTICE Excel hasn't been activated. To keep using Excel without interruption, activate before Friday, October 22, 2021. Activate \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{16}{|l|}{ G6 } \\ \hline & A & B & C & D & E & F & G & H & I & J & K & L & M & N & O \\ \hline 1 & SUMMARY OUTPUT & & & & & & & & & & & & & & \\ \hline \multicolumn{16}{|l|}{2} \\ \hline 3 & \multicolumn{15}{|c|}{ Regression Statistics } \\ \hline 4 & Multiple R & .524 & & & & & & & & & & & & & \\ \hline 5 & R Square & .274 & & & & & & & & & & & & & \\ \hline 6 & Adjusted R Square & .261 & & & & & & & & & & & & & \\ \hline 7 & Standard Error & .731 & & & & & & & & & & & & & \\ \hline 8 & Observations & 115.000 & & & & & & & & & & & & & \\ \hline \multicolumn{16}{|l|}{9} \\ \hline 10 & ANOVA & & & & & & & & & & & & & & \\ \hline 11 & & df & SS & MS & F & Significance F & & & & & & & & & \\ \hline 12 & Regression & 2.000 & 22.609 & 11.304 & 21.173 & .000 & & & & & & & & & \\ \hline 13 & Residual & 112.000 & 59.796 & .534 & & & & & & & & & & & \\ \hline 14 & Total & 114.000 & 82.405 & & & & & & & & & & & & \\ \hline \multicolumn{16}{|l|}{15} \\ \hline 16 & & Coefficients & Standard Error & t Stat & P-value & Lower 95\% & Upper 95\% & Lower 95.0% & Upper 95.0% & & & & & & \\ \hline 17 & Intercept & 1.363 & .378 & 3.602 & .000 & .613 & 2.113 & .613 & 2.113 & & & & & & \\ \hline 18 & StructuredInterview & .208 & .109 & 1.906 & .059 & -.008 & .424 & -.008 & .424 & & & & & & \\ \hline 19 & WorkSample & .220 & .036 & 6.049 & .000 & .148 & .292 & .148 & .292 & & & & & & \\ \hline \multicolumn{16}{|l|}{20} \\ \hline \multicolumn{16}{|l|}{21} \\ \hline 22 & & & & & & & & & & & & & & & \\ \hline \end{tabular}