# Nemsys LLC - Multiple Regression

## by Christopher Pappas on Dec 06, 2008

• 2,044 views

### Categories

Uploaded via SlideShare as Microsoft PowerPoint

### 5 Embeds19

 http://www.slideshare.net 9 http://www.christopher-pappas.com 5 http://www.skilltodocomesofdoing.com 3 http://static.slideshare.net 1 https://usm.blackboard.com 1

### Statistics

Likes
0
40
0
Embed Views
19
Views on SlideShare
2,025
Total Views
2,044

## Nemsys LLC - Multiple RegressionPresentation Transcript

• A regression analysis by: Christopher Pappas Gregory Davis Malcolm Campbell Iris Hu Amanda Zabriski
• 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
• 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
• 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
• 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
• Lawfirm
• Average age of workstations
• Ratio of laptops to overall workstations
•
• 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
• 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
• 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
• Used a squared predictor
• Get more data
•