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# Nemsys LLC - Multiple Regression

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### Nemsys LLC - Multiple Regression

1. 1. A regression analysis by: Christopher Pappas Gregory Davis Malcolm Campbell Iris Hu Amanda Zabriski
2. 2. <ul><li>Predict the monthly engineer hours required to service a prospective client </li></ul><ul><li>Better objectify certain cost factors </li></ul><ul><li>Utilize results to assist NEMSYS in increasing efficiency and/or effectiveness </li></ul>
3. 3. <ul><li>Every business today needs computer technology </li></ul><ul><li>Impractical for every company to hire the proper employees needed to maintain working technology </li></ul><ul><li>Service companies such as NEMSYS provide a cost-effective and efficient way to keep technology in working order </li></ul>
4. 4. <ul><li>Interviewed executives at NEMSYS to understand the main drivers of engineer hours </li></ul><ul><li>Collected NEMSYS client data </li></ul><ul><ul><li>Breakdown of monthly service hours for past 2 years </li></ul></ul><ul><li>Collected predictor data </li></ul><ul><li>Performed regression analysis </li></ul>
5. 5. <ul><li>The regression equation is: AMH = 27.0 - 14.1 S + 0.492 WS + 0.69 NP + 5.53 AS - 13.0 NC + 0.201 NP 2 </li></ul><ul><ul><li>AMH = avg monthly engineer hours </li></ul></ul><ul><ul><li>S = # of servers </li></ul></ul><ul><ul><li>WS = # of workstations </li></ul></ul><ul><ul><li>NP = # of network printer </li></ul></ul><ul><ul><li>AS = avg savvy </li></ul></ul><ul><ul><li>NC = avg network complexity </li></ul></ul><ul><ul><li>NP 2 = network printer squared </li></ul></ul>
6. 6. <ul><li>Lawfirm </li></ul><ul><li>Average age of workstations </li></ul><ul><li>Ratio of laptops to overall workstations </li></ul>
7. 8. <ul><li>Analysis: </li></ul><ul><li>Predictor Coef SE Coef T P </li></ul><ul><li>Constant 26.96 13.25 2.04 0.076 </li></ul><ul><li>S -14.092 6.361 -2.22 0.058 </li></ul><ul><li>WS 0.4918 0.1158 4.25 0.003 </li></ul><ul><li>NP 0.687 3.276 0.21 0.839 </li></ul><ul><li>AS 5.527 4.353 1.27 0.240 </li></ul><ul><li>NC -13.041 6.586 -1.98 0.083 </li></ul><ul><li>NP^2 0.2012 0.4468 0.45 0.664  </li></ul><ul><li>S = 6.35500 R-Sq = 81.5% R-Sq(adj) = 67.6% </li></ul><ul><li>  </li></ul><ul><li>Analysis of Variance </li></ul><ul><li>Source DF SS MS F P </li></ul><ul><li>Regression 6 1423.56 237.26 5.87 0.013 </li></ul><ul><li>Residual Error 8 323.09 40.39 </li></ul><ul><li>Total 14 1746.65 </li></ul>
8. 9. <ul><li>Limited in the amount of data available </li></ul><ul><li>Based on the rule of 6, the minimal amount of data to be used in the model should be 84 clients </li></ul><ul><ul><li>NEMSYS is a small company; does not service that many clients monthly </li></ul></ul><ul><ul><li>Fewer observations skews the R-squared towards 1, but you really haven’t explained the variation </li></ul></ul>
9. 10. <ul><li>Predict the monthly engineer hours required to service a prospective client </li></ul><ul><ul><li>AMH = 27.0 - 14.1 (1) + 0.492 (20) + 0.69 (2) + 5.53 (1) - 13.0 (0) + 0.201 (2 2 ) = 30.45 * \$85/hour = \$2,588.59 </li></ul></ul><ul><ul><li>Prediction interval: (16.59, 43.43) * \$85/hour = (\$1,410.15, \$3,691.55) </li></ul></ul><ul><ul><li>Conclusion: more data needed </li></ul></ul><ul><li>Better objectify certain cost factors </li></ul><ul><ul><li>YES </li></ul></ul><ul><li>Utilize results to assist NEMSYS in increasing efficiency and/or effectiveness </li></ul><ul><ul><li>YES </li></ul></ul>
10. 11. <ul><li>Used a squared predictor </li></ul><ul><li>Get more data </li></ul>