copyright © 2012 pearson education, inc. all rights reserved. chapter 8 residual analysis
TRANSCRIPT
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Chapter 8
Residual Analysis
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Section 8.1
Introduction
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Section 8.2
Regression Residuals
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Figure 8.1 Actual random error and regression residual ̂
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Figure 8.2 SAS printout for first-order model
continued on next slide
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Figure 8.2 SAS printout for first-order model (cont’d)
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Figure 8.3 SAS printout for quadratic (second-order) model
continued on next slide
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Figure 8.3 SAS printout for quadratic (second-order) model (cont’d)
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Section 8.3
Detecting Lack of Fit
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Figure 8.4 SAS plot of residuals for the first-order model
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Figure 8.5 MINITAB plot of cholesterol data with least squares line
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Figure 8.6 SAS plot of residuals for the quadratic model
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Figure 8.7 SPSS regression printout for demand model
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Figure 8.8 SPSS plot of residuals against price for demand model
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Figure 8.9 SPSS partial residual plot for price
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Figure 8.10 SPSS regression printout for demand model with transformed price
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Section 8.4
Detecting Unequal Variances
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Figure 8.11 A plot of residuals for poisson data
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Figure 8.12 A plot of residuals for binomial data (proportions or percentages)
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Figure 8.13 A plot of residuals for data subject to multiplicative errors
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Figure 8.14 MINITAB regression printout for second-order model of salary
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Figure 8.15 MINITAB residual plot for second-order model of salary
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Figure 8.16 MINITAB regression printout for second-order model of natural log of salary
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Figure 8.17 MINITAB residual plot for second-order model of natural log of salary
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Figure 8.18 MINITAB regression printout first-order model of natural log of salary
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Figure 8.19a SAS regression printout for second-order model of salary: Subsample 1 (years of experience < 20)
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Figure 8.19b SAS regression printout for second-order model of salary: Subsample 2 (years of experience > 20)
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Section 8.5
Checking the Normality Assumption
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Figure 8.20 MINITAB histogram of residuals from log model of salary
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Figure 8.21 MINITAB stem-and-leaf plot of residuals from log model of salary
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Figure 8.22 MINITAB normal probability plot of residuals from log model of salary
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Section 8.6
Detecting Outliners and Identifying Influential Observations
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Figure 8.23 MINITAB regression printout for model of fast-food sales
continued on next slide
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Figure 8.23 MINITAB regression printout for model of fast-food sales (cont’d)
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Figure 8.24 MINITAB plot of residuals versus traffic flow
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Figure 8.25 MINITAB plot of residuals versus city
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Figure 8.26 MINITAB regression printout for model of fast-food sales with corrected data point
continued on next slide
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Figure 8.26 MINITAB regression printout for model of fast-food sales with corrected data point (cont’d)
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Figure 8.27 MINITAB plot of residuals versus traffic flow for model with corrected data point
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Figure 8.28 MINITAB plot of residuals versus city for model with corrected data point
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Figure 8.29 SAS regression analysis with influence diagnosis for fast-food sales model
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Figure 8.29 SAS regression analysis with influence diagnosis for fast-food sales model (cont’d)
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Figure 8.29 SAS regression analysis with influence diagnosis for fast-food sales model (cont’d)
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Section 8.7
Detecting Residual Correlation: The Durbin-Watson test
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Figure 8.30 SAS regression printout for model of annual sales
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Figure 8.31 SAS plot of residuals for model of annual sales
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Figure 8.32 Rejection region for the Durbin-Watson d-test: scale example
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