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comparison of regression models

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Overheard at the water cooler: My regression model of demand is better than the one that the consultant prepared for us because it has a higher R2. Besides, my equation has three more independent variables and so is more complete then the consultant's. Comment on this statement. Would you agree with the speaker? Explain.

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The correlation coefficient, or R squared, measures the measures the strength of the linear association between a dependent and a number of independent variables. Increasing the number of independent variables in a multiple regression will generally increase the R^2, but it isn't a good way to do it. ...

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Comparison of regression models based on R squared.

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Develop a multiple linear regression model and explain how does this compare with simple regression.

Develop a multiple linear regression model.

A sample of twenty automobiles was taken, and the miles per gallon (MPG), horsepower and total weight were recorded. Develop a linear regression model to predict MPG using horsepower as the only independent variable. Develop another model with weight as the independent variable. Which of these two models is better? Explain.

MPG Horsepower Weight
44 67 1844
44 50 1998
40 62 1752
37 66 1797
34 63 2199
35 90 2404
32 99 2611
30 63 3236
28 91 2606
26 94 2580
26 88 2507
25 124 2922
22 97 2434
20 114 3248
21 102 2812
18 114 3382
18 142 3197
16 153 4380
16 139 4036

(Note: The data provided contains 19 values only)

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