The document summarizes a regression analysis conducted to predict the monthly engineer hours required to service clients for NEMSYS, a service company that maintains technology. Interviews and client data were collected on various predictors and a regression equation was developed. The analysis found the model explained 67.6% of the variation but more data is needed due to the small number of clients. The results can help NEMSYS improve efficiency if used with more data.

1. A regression analysis by: Christopher Pappas Gregory Davis Malcolm Campbell Iris Hu Amanda Zabriski

2. Predict the monthly engineer hours required to service a prospective client Better objectify certain cost factors Utilize results to assist NEMSYS in increasing efficiency and/or effectiveness

3. Every business today needs computer technology Impractical for every company to hire the proper employees needed to maintain working technology Service companies such as NEMSYS provide a cost-effective and efficient way to keep technology in working order

4. Interviewed executives at NEMSYS to understand the main drivers of engineer hours Collected NEMSYS client data Breakdown of monthly service hours for past 2 years Collected predictor data Performed regression analysis

5. 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 AMH = avg monthly engineer hours S = # of servers WS = # of workstations NP = # of network printer AS = avg savvy NC = avg network complexity NP 2 = network printer squared

6. Lawfirm Average age of workstations Ratio of laptops to overall workstations

8. Analysis: Predictor Coef SE Coef T P Constant 26.96 13.25 2.04 0.076 S -14.092 6.361 -2.22 0.058 WS 0.4918 0.1158 4.25 0.003 NP 0.687 3.276 0.21 0.839 AS 5.527 4.353 1.27 0.240 NC -13.041 6.586 -1.98 0.083 NP^2 0.2012 0.4468 0.45 0.664 S = 6.35500 R-Sq = 81.5% R-Sq(adj) = 67.6% Analysis of Variance Source DF SS MS F P Regression 6 1423.56 237.26 5.87 0.013 Residual Error 8 323.09 40.39 Total 14 1746.65

9. Limited in the amount of data available Based on the rule of 6, the minimal amount of data to be used in the model should be 84 clients NEMSYS is a small company; does not service that many clients monthly Fewer observations skews the R-squared towards 1, but you really haven’t explained the variation

10. Predict the monthly engineer hours required to service a prospective client 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 Prediction interval: (16.59, 43.43) * $85/hour = ($1,410.15, $3,691.55) Conclusion: more data needed Better objectify certain cost factors YES Utilize results to assist NEMSYS in increasing efficiency and/or effectiveness YES

11. Used a squared predictor Get more data