nemsys llc - multiple regression
TRANSCRIPT
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A regression analysis by:Christopher Pappas
Gregory DavisMalcolm Campbell
Iris HuAmanda Zabriski
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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
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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
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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
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The regression equation is: AMH = 27.0 - 14.1 S + 0.492 WS + 0.69 NP + 5.53 AS - 13.0 NC + 0.201 NP2
AMH = avg monthly engineer hours S = # of servers WS = # of workstations NP = # of network printer AS = avg savvy NC = avg network complexity NP2 = network printer squared
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Lawfirm Average age of workstations Ratio of laptops to overall workstations
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1050-5-10
99
90
50
10
1
Residual
Perc
ent
5040302010
10
5
0
-5
-10
Fitted Value
Resi
dual
1050-5
4
3
2
1
0
Residual
Fre
quency
151413121110987654321
10
5
0
-5
-10
Observation Order
Resi
dual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for average month hrs
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Analysis:Predictor Coef SE Coef T PConstant 26.96 13.25 2.04 0.076S -14.092 6.361 -2.22 0.058WS 0.4918 0.1158 4.25 0.003NP 0.687 3.276 0.21 0.839AS 5.527 4.353 1.27 0.240NC -13.041 6.586 -1.98 0.083NP^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 PRegression 6 1423.56 237.26 5.87 0.013Residual Error 8 323.09 40.39Total 14 1746.65
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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
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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 (22) = 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
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Used a squared predictor
Get more data
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