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regression analysis

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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

Looking into the Analysis of Variance, the P-value 0.000 (< 0.005) makes the whole model...

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