Methods and applications of linear models : regression and the analysis of variance /
A popular statistical text now updated and better than ever! The ready availability of high-speed computers and statistical software encourages the analysis of ever larger and more complex problems while at the same time increasing the likelihood of improper usage. That is why it is increasingly imp...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, N.J. :
Wiley-Interscience,
©2003.
|
| Edition: | 2nd ed. |
| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 100 | 1 | |a Hocking, R. R. |q (Ronald R.), |d 1932- | |
| 245 | 1 | 0 | |a Methods and applications of linear models : |b regression and the analysis of variance / |c Ronald R. Hocking. |
| 250 | |a 2nd ed. | ||
| 260 | |a Hoboken, N.J. : |b Wiley-Interscience, |c ©2003. | ||
| 300 | |a 1 online resource (xxi, 741 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Wiley EBA |5 TMurS | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 8 | |a 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. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a A popular statistical text now updated and better than ever! The ready availability of high-speed computers and statistical software encourages the analysis of ever larger and more complex problems while at the same time increasing the likelihood of improper usage. That is why it is increasingly important to educate end users in the correct interpretation of the methodologies involved. Now in its second edition, Methods and Applications of Linear Models: Regression and the Analysis of Variance seeks to more effectively address the analysis of such models through several important changes. | ||
| 520 | |a Notable in this new edition:Fully updated and expanded text reflects the most recent developments in the AVE methodRearranged and reorganized discussions of application and theory enhance text's effectiveness as a teaching toolMore than 100 new exercises in the areas of regression and analysis of varianceAs in the First Edition, the author presents a thorough treatment of the concepts and methods of linear model analysis, and illustrates them with various numerical and conceptual examples, using a data-based approach to development and analysis. Data sets, available on an FTP site, allow readers to apply analytical methods discussed in the book. | ||
| 650 | 0 | |a Regression analysis. | |
| 650 | 0 | |a Analysis of variance. | |
| 650 | 0 | |a Linear models (Statistics) | |
| 650 | 2 | |a Regression Analysis | |
| 650 | 2 | |a Linear Models | |
| 650 | 2 | |a Analysis of Variance | |
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