Tom's Used Mustangs: Regression Analysis

Regression Analysis (Tom’s Used Mustangs)
Irving Campus

GM 533: Applied Managerial Statistics

04/19/2012

Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35 used Mustangs and 10 different characteristics.
The test hypothesis that price is dependent on whether the car is convertible is superior to the other hypothesis tests conducted. The analysis performed showed that the test hypothesis with the smallest P-value was favorable, convertible cars had the smallest P-value.
The
P-value is 0.000; this means that there is extremely strong evidence of a relationship between the price of a used Mustang and the mileage of the car. We used the critical value F.05 based on 1 numerator and 33 denominator degrees of freedom. From the F table we find that at 95% critical value for Price vs. Miles = 4.139 while F = 42.34. Since the F (model) for Price vs. Miles = 42.34 which is greater than F.05 =4.139 we can reject the null hypothesis in favor of the alternative hypothesis at level of significance 0.05. The test tells us that if we reject the null hypothesis then we have evidence of a relationship between the prices vs. Miles of the used Mustangs. The Small P-value for the F test is evidence of a significant relationship between the
We used the critical value F.05 based on 1 numerator and 33 denominator degrees of freedom. From the F table we find that at 95% critical value for Price vs. Age = 4.139 while F = 68.20. Since the F (model) for Price vs. Age = 68.20 which is greater than F.05 =4.139 we can reject the null hypothesis in favor of the alternative hypothesis at level of significance 0.05.
ANALYSIS OF RESIDUALS
The following analysis of residuals was performed based on residual plots for prices against three predictors; miles, age and color. The residual plots tell us is that there isn't a violation of the regression assumptions, in the regression of demand on miles, age and color.

MULTICOLLINEARITY
Multicollinearity occurs if the independent variables in a regression situation if these independent variables are related to or dependent on each other. The data set would not be considered severe because none of the independent variables are at .9

Correlations: MILES, AGE, COLOR

MILES AGE
AGE 0.724