Lecture 7 lecture notes

Lecture 7 lecture notes - Lecture 7. Outliers and...

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Unformatted text preview: Lecture 7. Outliers and influential observations Example . Again we consider the Albuquerque Home Prices. Now we shall evaluate outliers and influential observations. Recall our MLR model: > l<-lm(data$PRICE~data$SQFT+data$AGE+data$CUST) > summary(l) Call: lm(formula = data$PRICE ~ data$SQFT + data$AGE + data$CUST) Residuals: Min 1Q Median 3Q Max-56871.580-9368.399 2.080 8506.354 63302.217 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7248.077 8452.629 0.857 0.39437 data$SQFT 63.914 4.712 13.565 < 2e-16 *** data$AGE-428.913 168.111-2.551 0.01313 * data$CUST 14931.462 5372.717 2.779 0.00715 **--- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 17420 on 64 degrees of freedom (49 observations deleted due to missingness) Multiple R-squared: 0.8216, Adjusted R-squared: 0.8132 F-statistic: 98.22 on 3 and 64 DF, p-value: < 2.2e-16 To evaluate outliers and influential observations, we use the generic function influence.measures . > infl<-influence.measures(l) > summary(infl) Potentially influential observations of lm(formula = 100 * data$PRICE ~ data$SQFT + data$AGE + data$CUST) : dfb.1_ dfb.d$SQ dfb.d$AG dfb.d$CU dffit cov.r cook.d hat 7 0.05-0.08 0.03 0.05-0.09 1.22_* 0.00 0.13 27 0.29-0.33-0.12 0.20-0.37 1.23_* 0.03 0.17 50 0.73-0.67-0.42-0.41-1.22_* 0.52_* 0.31 0.09 89 -0.1389 -0....
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Lecture 7 lecture notes - Lecture 7. Outliers and...

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