Linear models with R /

"Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing dat...

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Bibliographic Details
Main Author:	Faraway, Julian James (Author)
Format:	Book
Language:	English
Published:	Boca Raton : CRC Press, Taylor & Francis Group, [2015] Boca Raton : CRC Press, [2015] Boca Raton, FL : CRC Press/Taylor & Francis Group, [2015] Boca Raton, FL : [2015]
Edition:	Second edition
Series:	Texts in statistical science
Subjects:	Analysis of variance R (Computer program language) > Mathematical models Regression analysis Lineares Modell Regressionsanalys Regressionsanalyse Variansanalys Varianzanalyse r Lineaire modellen R (computerprogramma) Statistische analyse

Table of Contents:

1. Introduction
2. Estimation
3. Inference
4. Prediction
5. Explanations
6. Diagnostics
7. Problems with the predictors
8. Problems with the errors
9. Transformation
10. Model selection
11. Shrinkage methods
12. Insurance redlining
a complete example
13. Missing data
14. Categorical predictors
15. One factor models
16. Models with several factors
17. Experiments with blocks
Appendix A. About R
note: 1 Introduction
1.1. Before You Start
1.2. Initial Data Analysis
1.3. When to Use Linear Modeling
1.4. History
2. Estimation
2.1. Linear Model
2.2. Matrix Representation
2.3. Estimating β
2.4. Least Squares Estimation
2.5. Examples of Calculating β
2.6. Example
2.7. QR Decomposition
2.8. Gauss--Markov Theorem
2.9. Goodness of Fit
2.10. Identifiability
2.11. Orthogonality
3. Inference
3.1. Hypothesis Tests to Compare Models
3.2. Testing Examples
3.3. Permutation Tests
3.4. Sampling
3.5. Confidence Intervals for β
3.6. Bootstrap Confidence Intervals
4. Prediction
4.1. Confidence Intervals for Predictions
4.2. Predicting Body Fat
4.3. Autoregression
4.4. What Can Go Wrong with Predictions?
5. Explanation
5.1. Simple Meaning
5.2. Causality
5.3. Designed Experiments
5.4. Observational Data
5.5. Matching
5.6. Covariate Adjustment
5.7. Qualitative Support for Causation
6. Diagnostics
6.1. Checking Error Assumptions
6.1.1. Constant Variance
6.1.2. Normality
6.1.3. Correlated Errors
6.2. Finding Unusual Observations
6.2.1. Leverage
6.2.2. Outliers
6.2.3. Influential Observations
6.3. Checking the Structure of the Model
6.4. Discussion
7. Problems with the Predictors
7.1. Errors in the Predictors
7.2. Changes of Scale
7.3. Collinearity
8. Problems with the Error
8.1. Generalized Least Squares
8.2. Weighted Least Squares
8.3. Testing for Lack of Fit
8.4. Robust Regression
8.4.1. M-Estimation
8.4.2. Least Trimmed Squares
9. Transformation
9.1. Transforming the Response
9.2. Transforming the Predictors
9.3. Broken Stick Regression
9.4. Polynomials
9.5. Splines
9.6. Additive Models
9.7. More Complex Models
10. Model Selection
10.1. Hierarchical Models
10.2. Testing-Based Procedures
10.3. Criterion-Based Procedures
10.4. Summary
11. Shrinkage Methods
11.1. Principal Components
11.2. Partial Least Squares
11.3. Ridge Regression
11.4. Lasso
12. Insurance Redlining
- A Complete Example
12.1. Ecological Correlation
12.2. Initial Data Analysis
12.3. Full Model and Diagnostics
12.4. Sensitivity Analysis
12.5. Discussion
13. Missing Data
13.1. Types of Missing Data
13.2. Deletion
13.3. Single Imputation
13.4. Multiple Imputation
14. Categorical Predictors
14.1. Two-Level Factor
14.2. Factors and Quantitative Predictors
14.3. Interpretation with Interaction Terms
14.4. Factors With More Than Two Levels
14.5. Alternative Codings of Qualitative Predictors
15. One Factor Models
15.1. Model
15.2. Example
15.3. Diagnostics
15.4. Pairwise Comparisons
15.5. False Discovery Rate
16. Models with Several Factors
16.1. Two Factors with No Replication
16.2. Two Factors with Replication
16.3. Two Factors with an Interaction
16.4. Larger Factorial Experiments
17. Experiments with Blocks
17.1. Randomized Block Design
17.2. Latin Squares
17.3. Balanced Incomplete Block Design.

Linear models with R /

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