regression analysis

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 &#12288; 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(&#65505;0&#65289; versus Machine Hours The regression equation is Maintenance Costs(&#65505;0&#65289; = 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