Kutner, et al., Applied Linear Statistical Models is an excellent text on performing statistical analyses without requiring the deeper mathematical statistics behind it. The text is rich in examples, often from a variety of software sources. The UCLA Academic Technology Services Statistical Computing department hosts their own scripts for reproducing the examples for the 4th edition using SAS or Stata. Here Bryan has taken it upon himself to reproduce the examples for the 5th edition using R. This has been an ongoing project to help him learn how to do applied statistics and to use R.
While the data can be found online, it and the available scripts have been included in a < 200KB archive.
Download Scripts + Data Archive
This is actually the 2nd set of code. It was edited after Bryan reached the end of part 2 and realized he could do a lot better with the code given what he had learned about R at that time. No doubt, Bryan will end up redoing this 3 or 4 times as he gets better. For instance, Bryan wants to introduce lattice and ggplot2 graphics at certain portions of these guides. The interested reader is urged to not only review the code here for its approaches, but to think about how to do them better. In generating the code for yourself, you learn so much more. If you find a better way, please send a message with your revisions, and you will be include in an acknowledgment here.
—– TABLE OF CONTENTS —–
Part 1 | Simple Linear Regression
Chapter 1 Linear Regression with One Predictor Variable
Chapter 2 Inferences in Regression and Correlation Analysis
Chapter 3 Diagnostics and Remedial Measures
Chapter 4 Simultaneous Inferences and Other Topics in Regression Analysis
Chapter 5 Matrix Approach to Simple Linear Regression Analysis
Part 2 | Multiple Linear Regression
Chapter 6 Multiple Regression I
Chapter 7 Multiple Regression II
Chapter 8 Regression Models for Quantitative and Qualitative Predictors
Chapter 9 Building the Regression Model I: Model Selection and Validation
Chapter 10 Building the Regression Model II: Diagnostics
Chapter 11 Building the Regression Model III: Remedial Measures
Chapter 12 Autocorrelation in Time Series Data
Part 3 | Nonlinear Regression
Chapter 13 Introduction to Nonlinear Regression and Neural Networks
Chapter 14 Logistic Regression, Poisson Regression, and Generalized Linear Models
Part 4 | Design and Analysis of Single-Factor Studies
Chapter 15 Introduction to the Design of Experimental and Observational Studies
Chapter 16 Single-Factor Studies
Chapter 17 Analysis of Factor Level Means
Chapter 18 ANOVA Diagnostics and Remedial Measures
Part 5 | Multi-Factor Studies
Chapter 19 Two-Factor Studies with Equal Sample Sizes
Chapter 20 Two-Factor Studies–One case per Treatment
Chapter 21 Randomized Complete Block
Chapter 22 Analysis of Covariance
Chapter 23 Two-Factor Studies with Unequal Sample Sizes
Chapter 24 Multi-Factor Studies
Chapter 25 Random and Mixed Effects Models
Part 6 | Specialized Study Designs
Chapter 26 Nested Designs, Subsampling, and Partially Nested Designs
Chapter 27 Repeated Measures and Related Designs
Chapter 28 Balanced Incomplete Block, Latin Square, and Related Designs
Chapter 29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs
Chapter 30 Response Surface Methodology
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To be added later: Useful links, references, and other R related information.
