You just learned how to create a correlation matrix and to run a regression analysis in Excel. Now, its time to apply your new skills to a new data set, which appears in the sheet called Practice in the Excel workbook. This time, your team is assessing the criterion-related validities for a scored structured interview (StructuredInterview) and work sample assessment (WorkSample) in relation to overall job performance (JobPerformance) for a Software Developer job. You have already established that both selection procedures are significantly associated with job performance by themselves (as evidenced by correlation analyses), and now its time to assess whether there is any redundancy between the two selection procedure variables (i.e., very large correlation) if both are still significantly associated with job performance when specified in the same regression model, and what the collective criterion-related validity of the selection procedures are in terms of the R-squared value. 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?

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-square 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?

\begin{tabular}{|l|l|l|} \hline & \multicolumn{1}{|c}{ A } & \multicolumn{2}{c}{ B } \\ \hline 1 & \multicolumn{1}{|c|}{ SUMMARY OUTPUT } \\ \hline 2 & \multicolumn{2}{|l|}{} \\ \hline 3 & \multicolumn{2}{|c|}{ Regression Statistics } \\ \hline 4 & Multiple R & .524 \\ \hline 5 & R Square & .274 \\ \hline 6 & Adjusted R Sq & .261 \\ \hline 7 & Standard Erro & .731 \\ \hline 8 & Observations & 115.000 \\ \hline \end{tabular} ANOVA \begin{tabular}{l|l|r|r|r|r|r} \hline 11 & \multicolumn{1}{|c|}{df} & \multicolumn{1}{|c|}{ SS } & \multicolumn{1}{c|}{MS} & \multicolumn{1}{c|}{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 & & & & & \\ \hline \end{tabular} \begin{tabular}{|l|l|r|r|r|} \hline & \multicolumn{1}{|c|}{ A } & \multicolumn{1}{c|}{ B } & \multicolumn{1}{c|}{ C } & \multicolumn{1}{c|}{ D } \\ \hline 1 & \multicolumn{1}{|l|}{ WorkSample } & icturedintervijbbPerformance \\ \hline 2 & WorkSample & 1 & & \\ \hline 3 & Structuredlnt & 0.08013418 & 1 & 1 \\ \hline 4 & JobPerformar & 0.5008175 & 0.19307964 & 1 \\ \hline \end{tabular}