Linear Models with R
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Table of contents:Introduction. Estimation. Inference. Errors in Predictors. Generalized Least Squares. Testing for Lack of Fit. Diagnostics. Transformation/ Scale Changes, Principal Components and Collinearity. Variable Selection. Statistical Strategy and Model Uncertainty. Chicago Insurance Redlining - a Complete Example. Robust and Resistant Regression. Missing Data. Analysis of Covariance. ANOVA. Recommended Books. R Functions and Data. Quick Introduction to R.In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analy...
Table of contents:
Introduction. Estimation. Inference. Errors in Predictors. Generalized Least Squares. Testing for Lack of Fit. Diagnostics. Transformation/ Scale Changes, Principal Components and Collinearity. Variable Selection. Statistical Strategy and Model Uncertainty. Chicago Insurance Redlining - a Complete Example. Robust and Resistant Regression. Missing Data. Analysis of Covariance. ANOVA. Recommended Books. R Functions and Data. Quick Introduction to R.
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from the Internet.
This textbook focuses on the practice of regression and analysis of variance. Students will learn which methods are available and the various situations in which they can be applied.
Introduction. Estimation. Inference. Errors in Predictors. Generalized Least Squares. Testing for Lack of Fit. Diagnostics. Transformation/ Scale Changes, Principal Components and Collinearity. Variable Selection. Statistical Strategy and Model Uncertainty. Chicago Insurance Redlining - a Complete Example. Robust and Resistant Regression. Missing Data. Analysis of Covariance. ANOVA. Recommended Books. R Functions and Data. Quick Introduction to R.
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from the Internet.
This textbook focuses on the practice of regression and analysis of variance. Students will learn which methods are available and the various situations in which they can be applied.