Linear models with R (Record no. 102346)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03626nam a22001697a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781439887332 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.5 FAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Faraway, Julian J |
| 245 ## - TITLE STATEMENT | |
| Title | Linear models with R |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New York |
| Name of publisher, distributor, etc. | CRC Press |
| Date of publication, distribution, etc. | 2015 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 274p. |
| Other physical details | 24cm; Hard |
| 500 ## - GENERAL NOTE | |
| General note | 1040/2nd Jan 2015<br/>Rs.6060/- |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Contents<br/>Preface xi<br/>1 Introduction 1<br/>1.1 Before You Start 1<br/>1.2 Initial Data Analysis 2<br/>1.3 When to Use Linear Modeling 7<br/>1.4 History 8<br/>2 Estimation 13<br/>2.1 Linear Model 13<br/>2.2 Matrix Representation 14<br/>2.3 Estimating β 15<br/>2.4 Least Squares Estimation 16<br/>2.5 Examples of Calculating ˆ<br/>β 17<br/>2.6 Example 17<br/>2.7 QR Decomposition 20<br/>2.8 Gauss–Markov Theorem 22<br/>2.9 Goodness of Fit 23<br/>2.10 Identifiability 26<br/>2.11 Orthogonality 28<br/>3 Inference 33<br/>3.1 Hypothesis Tests to Compare Models 33<br/>3.2 Testing Examples 35<br/>3.3 Permutation Tests 40<br/>3.4 Sampling 42<br/>3.5 Confidence Intervals for β 43<br/>3.6 Bootstrap Confidence Intervals 46<br/>4 Prediction 51<br/>4.1 Confidence Intervals for Predictions 51<br/>4.2 Predicting Body Fat 52<br/>4.3 Autoregression 54<br/>4.4 What Can Go Wrong with Predictions? 56<br/>5 Explanation 59<br/>5.1 Simple Meaning 59<br/>5.2 Causality 61<br/>5.3 Designed Experiments 62<br/>5.4 Observational Data 63<br/>5.5 Matching 65<br/>5.6 Covariate Adjustment 68<br/>5.7 Qualitative Support for Causation 69<br/>6 Diagnostics 73<br/>6.1 Checking Error Assumptions 73<br/>6.1.1 Constant Variance 73<br/>6.1.2 Normality 78<br/>6.1.3 Correlated Errors 81<br/>6.2 Finding Unusual Observations 83<br/>6.2.1 Leverage 83<br/>6.2.2 Outliers 85<br/>6.2.3 Influential Observations 89<br/>6.3 Checking the Structure of the Model 92<br/>6.4 Discussion 96<br/>7 Problems with the Predictors 99<br/>7.1 Errors in the Predictors 99<br/>7.2 Changes of Scale 103<br/>7.3 Collinearity 106<br/>8 Problems with the Error 113<br/>8.1 Generalized Least Squares 113<br/>8.2 Weighted Least Squares 116<br/>8.3 Testing for Lack of Fit 119<br/>8.4 Robust Regression 123<br/>8.4.1 M-Estimation 123<br/>8.4.2 Least Trimmed Squares 126<br/>9 Transformation 133<br/>9.1 Transforming the Response 133<br/>9.2 Transforming the Predictors 137<br/>9.3 Broken Stick Regression 137<br/>9.4 Polynomials 139<br/>9.5 Splines 141<br/>9.6 Additive Models 144<br/>9.7 More Complex Models 145<br/>10 Model Selection 149<br/>10.1 Hierarchical Models 150<br/>10.2 Testing-Based Procedures 151<br/>10.3 Criterion-Based Procedures 153<br/>10.4 Summary 159<br/>11 Shrinkage Methods 161<br/>11.1 Principal Components 161<br/>11.2 Partial Least Squares 172<br/>11.3 Ridge Regression 174<br/>11.4 Lasso 177<br/>12 Insurance Redlining — A Complete Example 183<br/>12.1 Ecological Correlation 183<br/>12.2 Initial Data Analysis 185<br/>12.3 Full Model and Diagnostics 188<br/>12.4 Sensitivity Analysis 190<br/>12.5 Discussion 194<br/>13 Missing Data 197<br/>13.1 Types of Missing Data 197<br/>13.2 Deletion 198<br/>13.3 Single Imputation 200<br/>13.4 Multiple Imputation 202<br/>14 Categorical Predictors 205<br/>14.1 A Two-Level Factor 205<br/>14.2 Factors and Quantitative Predictors 209<br/>14.3 Interpretation with Interaction Terms 212<br/>14.4 Factors With More Than Two Levels 213<br/>14.5 Alternative Codings of Qualitative Predictors 219<br/>15 One Factor Models 223<br/>15.1 The Model 223<br/>15.2 An Example 224<br/>15.3 Diagnostics 227<br/>15.4 Pairwise Comparisons 228<br/>15.5 False Discovery Rate 230<br/>16 Models with Several Factors 235<br/>16.1 Two Factors with No Replication 235<br/>16.2 Two Factors with Replication 239<br/>16.3 Two Factors with an Interaction 243<br/>16.4 Larger Factorial Experiments 246<br/>17 Experiments with Blocks 251<br/>17.1 Randomized Block Design 252<br/>17.2 Latin Squares 256<br/>17.3 Balanced Incomplete Block Design 259<br/>A About R 265<br/>Bibliography 267<br/>Index 271 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Analysis of variance |
| -- | Regression analysis |
| -- | R (Computer programming Language) |
| -- | R computer Programing Language |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GSB Collection | 02/01/2015 | 4 | 519.5 FAR | B1874 | 13/07/2021 | 25/06/2019 | 06/01/2015 | Books |