# Nemsys LLC - Multiple Regression

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Information about Nemsys LLC - Multiple Regression

Published on December 7, 2008

Author: CPappasOnline

Source: slideshare.net

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

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

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

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

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

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

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

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

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

Used a squared predictor

Get more data

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