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Unformatted text preview: d in log units: > predict(LogLog.model, newdata=data.frame(Circulation=c(0.5, 20)),interval="prediction") fit lwr upr 1 4.308227 3.947855 4.668600 2 6.258752 5.885815 6.631689 So for a circulation of .5 million we predict exp(4.308227)= 74.3, lwr=51.8, upr=106.5 For circulation of 20 million fit=522.6, lwr=359.9, upr = 758.8 c) Weaknesses in model: The condition of constant variance is possibly not satisfied. There are some potentially influential/high leverage points: 4, 8, 49 stand out. > quality=influence.measures(LogLog.model) > summary(quality) If you didn't run this pair of commands, but relied only on the fourth diagnostic plot provided by plot(LogLog.model), you'd have seen observations 4 and 49 flagged for potential investigation. Question two part b. Not required. We have not yet covered polynomials. Question three. Using cars4.csv from the book answer the following questions. Part a)
• Create an R output for the prediction suggested retail price (Y) from dealer cost
(X).
> m1< lm(SuggestedRetailPrice ~ DealerCost)
> summary(m1)
Call:
lm(formula = SuggestedRetailPrice ~ DealerCost)
Residuals:
Min
1Q Median
3Q
Max
1743.52 262.59 74.92 265.98 2912.72
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 61.904248 81.801381 0.757 0.45
DealerCost 1.088841 0.002638 412.768 <2e16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 587 on 232 degrees of freedom
Multiple Rsquared: 0.9986, Adjusted Rsquared: 0.9986
Fstatistic: 1.704e+05 on 1 and 232 DF, pvalue: < 2.2e16
• Create the relevant plots to check fo...
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 Fall '11
 MahtashEsfandiari
 Statistics

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