1203 WordsApr 26, 20095 Pages

Modeling and Evaluating
Regression Analysis Report
Introduction
This report is first going to construct the best model to track maintenance cost of the existing production line for Aberdour Foods plc together with examining the structure of the maintenance cost. Then the assumptions and limitations of the selected model will be discussed respectively. Last, we will have a glance at the related guidance for potential use of the machines by future operation.
Model evaluation
Simple plotting
The two scatter plots above indicate a positive linear relationship between maintenance costs and machine hours, as well as Bulk bins. To compare, the machine hours seems to have a slightly stronger linear relationship with maintenance costs. This result suggests that the machine hours might be the better predictor.
Correlation matrix
　 Maintenance Costs Machine Hours Bulk Bins
Maintenance Costs 1
Machine Hours 0.865551 1
Bulk Bins 0.815458 0.908056 1
The correlation analysis suggests that the machine hours has a stronger relationship with the dependent variance compare to the other independent variance which again proved to be the better estimator.
Simple regression
Regression Analysis: Maintenance Costs(￡0） versus Machine Hours
The regression equation is
Maintenance Costs(￡0） = 68.2 + 0.278 Machine Hours
Predictor Coef SE Coef T P
Constant 68.16 12.19 5.59 0.000
Machine Hours 0.27849 0.02763 10.08 0.000
S = 8.42900 R-Sq = 74.9% R-Sq(adj) = 74.2%
Analysis of Variance
Source DF SS MS F P
Regression 1 7215.3 7215.3 101.55 0.000
Regression Statistics
Multiple R 0.865551
R Square 0.749178
Adjusted R Square 0.741801
Standard Error 8.429005
Observations 36

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