101B_hw_4_answers_W12steven

The same is clear from the qqplot based on the plot

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Unformatted text preview: r linearity, normality, and constancy of variance. • D escribe the shortcomings of the model and what you might do to o vercome these s hortcomings. • • • Based on the plot of standardized residuals vs. DealerCost, there are two to outliers whose standardized value is higher than +2 (214 and 215) and one whose value is less than -2 (223). The same is clear from the qqplot. Based on the plot of standardized residuals vs. DealerCost, we see that there is a pattern in the data and the assumption of variance constancy is not met. Based on the plot of residuals vs. fitted observation 223 is a leverage (X value way above the mean of X) and also a standardized residual (Z values less than -2). P art b) • M ake a log transformation of the dealer cost and the suggested retail p rice. • C reate an R output for the prediction of the log(suggested retail p rice) from l og(dealer cost). > m2 <- lm(log(SuggestedRetailPrice)~log(DealerCost)) > summary(m2) Call: lm(formula = log(SuggestedRetailPrice) ~ log(DealerCost)) Residuals: Min 1Q Median 3Q Max -0.062920 -0.008694 0.000624 0.010621 0.048798 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.069459 0.026459 -2.625 0.00924 ** log(DealerCost) 1.014836 0.002616 387.942 < 2e-16 *** --Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.01865 on 232 degrees of freedom Multiple R-squared: 0.9985, Adjusted R-squared: 0.9985 F-statistic: 1.505e+05 on 1 and 232 DF, p-value: < 2.2e-16 • ( Y) and interpret the estimated coefficient of log(dealer cost) Foe every one percent increase in the DealerCost, there will be on 1.015% increase in the Suggested Retail Price. • C reate the relevant plots to check for lineari ty, normality, and c onstancy of v ariance. D escribe the improvements...
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This note was uploaded on 02/05/2014 for the course STAT 101A taught by Professor Mahtashesfandiari during the Fall '11 term at UCLA.

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