Regression diagnostics in (base) R

Regression models assume the errors are normal, independent, and have constant variance. In addition, these models assume a particular functional form between the expected response and the explanatory variables. This video discusses graphical techniques for assessing model assumptions. These diagnostics involve case statistics including leverage, (standardized) residuals, and Cook's Distance. Diagnostics plots are shown for a telomere data set.

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Probability playlist:    • STAT 587 - Probability  
Inference playlist:    • STAT 587 - Inference  
Regression playlist:    • STAT 587 - Regression  

STAT 587: https://www.jarad.me/courses/stat587Eng/
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Slides: https://www.jarad.me/courses/stat587E...

Chapters:
00:00 Introduction
00:25 All models are wrong!
03:00 Simple linear regression model assumptions
04:26 Multiple regression model assumptions
05:09 Telomere data
05:34 Case statistics
06:08 Default diagnostic plots in R
06:36 Leverage
08:14 Leverage for telomere data
08:52 Residuals and fitted values
10:00 Standardized residuals
11:46 Residuals for telomere data
12:04 Cook's distance
12:55 Residuals vs fitted values plot
14:20 QQ-plot
15:42 Absolute standardized residuals vs fitted values plot
16:20 Cook's distance vs observation number plot
16:45 Residuals vs leverage plot
17:20 Cook's distance vs leverage plot
17:35 Additional plots
18:15 Residuals vs index plot
18:45 Residuals vs explanatory variable plot
18:59 ggResidpanel R package
20:03 Summary


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