Screen LibraryMethods and applications of linear models regression and the analysis of variance /
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| Main Authors: | |
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| Published: |
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| Literature type: | Electronic eBook |
| Language: | English |
| Edition: | 2nd ed. |
| Series: |
Wiley series in probability and statistics
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| Subjects: | |
| Online Access: |
http://onlinelibrary.wiley.com/book/10.1002/0471434159 |
| Carrier Form: | 1 online resource (xxi, 741 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
0471458627 9780471458623 047123222X 9780471232223 0471434159 9780471434153 |
| Index Number: | QA278 |
| CLC: | O212 |
| Contents: | Cover -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Part 1 Regression Models -- 1 Introduction to Linear Models -- 1.1 Background Information -- 1.2 Mathematical and Statistical Models -- 1.3 Definition of the Linear Model -- 1.4 Examples of Regression Models -- 1.5 Concluding Comments -- Exercises -- 2 Regression on Functions of One Variable -- 2.1 Simple Linear Regression Model -- 2.2 Parameter Estimation -- 2.3 Properties of the Estimators -- 2.4 Analysis of the Simple Linear Regression Model -- 2.5 Examining the Data and the Model -- 2.6 Test for Lack of Fit -- 2.7 Polynomial Regression Models -- Exercises -- 3 Transforming the Data -- 3.1 Need for Transformations -- 3.2 Weighted Least Squares -- 3.3 Variance Stabilizing Transformations -- 3.4 Transformations to Achieve a Linear Model -- 3.5 Analysis of the Transformed Model -- 3.6 Transformations with Forbes Data -- Exercises -- 4 Regression on Functions of Several Variables -- 4.1 Multiple Linear Regression Model -- 4.2 Preliminary Data Analysis -- 4.3 Analysis of the Multiple Linear Regression Model -- 4.4 Partial Correlation and Added-Variable Plots -- 4.5 Variable Selection -- 4.6 Model Specification -- Exercises -- 5 Collinearity in Multiple Linear Regression -- 5.1 Collinearity Problem -- 5.2 Example With Collinearity -- 5.3 Collinearity Diagnostics -- 5.4 Remedial Solutions: Biased Estimators -- Exercises -- 6 Influential Observations in Multiple Linear Regression -- 6.1 Influential Data Problem -- 6.2 Hat Matrix -- 6.3 Effects of Deleting Observations -- 6.4 Numerical Measures of Influence -- 6.5 Dilemma Data -- 6.6 Plots for Identifying Unusual Cases -- 6.7 Robust/Resistant Methods in Regression Analysis -- Exercises -- 7 Polynomial Models and Qualitative Predictors -- 7.1 Polynomial Models -- 7.2 Analysis of Response Surfaces -- 7.3 Models with Qualitative Predictors -- Exercises -- 8 Additional Topics -- 8.1 Non-Linear Regression Models -- 8.2 Non-Parametric Model-Fitting Methods -- 8.3 Logistic Regression -- 8.4 Random Input Variables -- 8.5 Errors in the Inputs -- 8.6 Calibration -- Exercises -- Part II Analysis of Variance Models -- 9 Introduction to Analysis of Variance Models -- 9.1 Background Information -- 9.2 Cell Means Model -- 9.3 Fixed Effects Models -- 9.4 Mixed Effects Models -- 9.5 Concluding Comments -- Exercises -- 10 Fixed Effects Models I: One-way Classification of Means -- 10.1 Introduction -- 10.2 One- Way Classification: Balanced Data -- 10.3 One- Way Classification: Unbalanced Data -- 10.4 Analysis of Covariance -- Exercises -- 11 Fixed Effects Models II: Two-way Classification of Means -- 11.1 Unconstrained Model: Balanced Data -- 11.2 Unconstrained Model: Unbalanced Data -- 11.3 No-Interaction Model: Balanced Data -- 11.4 No-Interaction Model: Unbalanced Data -- 11.5 Non-Homogeneous Experimental Units: The Concept of Blocking -- Exercises -- 12 Fixed Effects Models III: Multiple Crossed and Nested Factors -- 12.1 Three-Factor Cross-Classified Model. |