case study 10
tonya0164
Case15.1-AnswerKey_Canvas.docxCase 15.1: Dynamic Scales, Inc.
The central issue in this case is the need to …. to accurately predict the … weight of trucks. If a “good” model can be developed, trucks determined to be within the acceptable weight limits will not be required to stop at the regular weigh stations.
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Let |
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Static weight |
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x1 = |
Dynamic weight |
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x2 = |
Truck speed |
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x3 = |
Temperature |
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x4 = |
moisture |
The multiple regression information from Excel is given below:
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Regression Analysis |
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Regression Statistics |
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Multiple R |
0.883688864 |
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R Square |
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Adjusted R Square |
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Standard Error |
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Observations |
30 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
4 |
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Residual |
25 |
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Total |
29 |
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Coefficients |
Standard Error |
t Stat |
P-value |
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Intercept |
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Front-Axle Dynamic Weight |
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Truck Speed |
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Temperature |
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Moisture |
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Note that only the slope coefficient for Front-Axle Dynamic Weight is significant. The other variables are not significant at any reasonable level of alpha as can be seen by looking at the p-value column. The overall model is …. and the adjusted R2 = …...
Students can use the stepwise regression feature of PhStat or XLSTAT to try to develop a better model. The results are as follows:
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Stepwise Analysis |
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Table of Results for General Stepwise |
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Front-Axle Dynamic Weight entered. |
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df |
SS |
MS |
F |
Significance F |
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Regression |
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Residual |
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Total |
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Coefficients |
Standard Error |
t Stat |
P-value |
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Intercept |
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Front-Axle Dynamic Weight |
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No other variables could be entered into the model. Stepwise ends. |
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This model is also significant and has an R2 of. The students might indicate that the company could want to explore other variables as possible independent variables.
