Car Project Report

Car Project Report

Luxury Coupe Market
1) Check the correlation coefficients table, and explain the strength of linear relationship between dependent variable and each independent variable.

Price Cyl HP Cs Wt EPA
Price 1
Cyl 0.8126 1
HP 0.81796 0.88496 1
Cs -0.05553 0.227818 0.210189 1
Wt 0.299822 0.583057 0.553415 0.721702 1
EPA -0.86167 -0.72173 -0.87161 -0.03383 -0.45652 1

Correlation between Price and RHS variables:
There is a strong positive correlation between price and the number of cylinders (Cyl) as well as Price and the automobile’s horse power (HP). On the other hand Price has a strong negative correlation with the estimated highway consumption mileage for the luxury coupe market. In summary Price has a positive linear relationship with the following RHS variables: Cyl, HP, Wt. and negative linear relationship with: Cs and EPA.

Correlation between each pair of RHS variables:
Multicollinearity occurs when the absolute value of the correlation coefficient between two independent variables is greater than 0.8. In our basic model above multicollinearity is observed between the number of cylnders (Cyl) and horse power (HP) 0.88496, which is makes perfect sense since the more cylinders an automobile has the more horse power it will carry under the hood. Multicollinearity is also noticed between horse power (HP) and highway mileage consumption (EPA) -0.87161, which again can be seen in the real as cars that carry lots of horse power lack fuel consumption on the road. The rest of the correlation coefficients between the independent variables reveal weak relationships between each of the RHS variables.

2) What are the regression equations? Explain the economic meanings of estimated coefficients. Does the regression make sense? Are the coefficients what you would expect, and in reasonable magnitudes?

Regression equation:
Pricei = β0 + β1Cyli + β2HPi + β3Csi + β4Wti + β5EPAi + ei

Pricei = 531693.8 + 19851.17Cyli – 202.279HPi +...

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