Methods and Applications of Linear Models Regression and the Analysis of Variance

Name: Methods and Applications of Linear Models Regression and the Analysis of Variance
Price: 4.81 USD
Availability: InStock
ISBN: 9780471592822

ISBN-10: 047159282X

ISBN-13: 9780471592822

Edition: 1st 1996

Authors: Ronald R. Hocking

List price: $105.00

30 day, 100% satisfaction guarantee!

Marketplace

2 new & used from $4.81

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

Written by R. R. Hocking, this book aims to present methods in a conceptually simple way so the reader may more easily understand the applications of the methods. It contains a good balance of theory and applications; a solutions manual and data sets disk.

Book details

List price: $105.00
Edition: 1st
Copyright year: 1996
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/27/1996
Binding: Hardcover
Pages: 768
Size: 6.50" wide x 9.75" long x 1.50" tall
Weight: 2.750
Language: English

Michael G. Shields is the librarian at the Lonergan Research Institute, Regis College, University of Toronto, and translator of several volumes in the Collected Works of Bernard Lonergan.



Preface to the Second Edition


Preface to the First Edition



Regression Models



Introduction to Linear Models



Background Information



Mathematical and Statistical Models



Definition of the Linear Model



Examples of Regression Models



Concluding Comments


Exercises



Regression on Functions of One Variable



Simple Linear Regression Model



Parameter Estimation



Properties of the Estimators



Analysis of the Simple Linear Regression Model



Examining the Data and the Model



Test for Lack of Fit



Polynomial Regression Models


Exercises



Transforming the Data



Need for Transformations



Weighted Least Squares



Variance Stabilizing Transformations



Transformations to Achieve a Linear Model



Analysis of the Transformed Model



Transformations with Forbes Data


Exercises



Regression on Functions of Several Variables



Multiple Linear Regression Model



Preliminary Data Analysis



Analysis of the Multiple Linear Regression Model



Partial Correlation and Added-Variable Plots



Variable Selection



Model Specification


Exercises



Collinearity in Multiple Linear Regression



Collinearity Problem



Example With Collinearity



Collinearity Diagnostics



Remedial Solutions: Biased Estimators


Exercises



Influential Observations in Multiple Linear Regression



Influential Data Problem



Hat Matrix



Effects of Deleting Observations



Numerical Measures of Influence



Dilemma Data



Plots for Identifying Unusual Cases



Robust/Resistant Methods in Regression Analysis


Exercises



Polynomial Models and Qualitative Predictors



Polynomial Models



Analysis of Response Surfaces



Models with Qualitative Predictors


Exercises



Additional Topics



Non-Linear Regression Models



Non-Parametric Model-Fitting Methods



Logistic Regression



Random Input Variables



Errors in the Inputs



Calibration


Exercises



Analysis of Variance Models



Introduction to Analysis of Variance Models



Background Information



Cell Means Model



Fixed Effects Models



Mixed Effects Models



Concluding Comments


Exercises



Fixed Effects Models I: One-Way Classification of Means



Introduction



One-Way Classification: Balanced Data



One-Way Classification: Unbalanced Data



Analysis of Covariance


Exercises



Fixed Effects Models II: Two-Way Classification of Means



Unconstrained Model: Balanced Data



Unconstrained Model: Unbalanced Data



No-Interaction Model: Balanced Data



No-Interaction Model: Unbalanced Data



Non-Homogeneous Experimental Units: The Concept of Blocking


Exercises



Fixed Effects Models III: Multiple Crossed and Nested Factors



Three-Factor Cross-Classified Model



General Structure for Balanced, Factorial Models



Two-Fold Nested Model



General Structure for Balanced, Nested Models



Three-Factor, Nested-Factorial Model



General Structure for Balanced, Nested-Factorial Models


Exercises



Mixed Models I: The AOV Method with Balanced Data



Introduction



One-Way Classification, Random Model



Two-Way Classification, Mixed Model



Three-Factor, Nested-Factorial Model



General Analysis for Balanced, Mixed Models



Additional Examples



Alternative Development of Mixed Models


Exercises



Mixed Models II: The AVE Method with Balanced Data



Introduction



Two-Way Cross-Classification Model



Two-Fold Nested Model



Three Factor, Nested-Factorial Model



General Description of the AVE Table



Additional Examples



Computational Procedure for the AVE Method



Properties of the AVE Estimates


Exercises



Mixed Models III: Unbalanced Data



Introduction



Parameter Estimation: Likelihood Methods



ML and REML Estimates with Balanced Data



EM Algorithm for REML Estimation



EM Algorithm Applied to the AVE Method



Models with Covariates



Summary


Exercises



Mathematical Theory of Linear Models



Distribution of Linear and Quadratic Forms



Linear and Quadratic Forms



Multivariate Normal Distribution



Distribution of Quadratic Forms



Other Non-Central Distributions


Exercises



Estimation and Inference for Linear Models



Estimation of Parameters



Tests of Linear Hypotheses on [beta]



Confidence Regions and Intervals


Exercises



Simultaneous Inference: Tests and Confidence Intervals



Simultaneous Tests



Simultaneous Confidence Intervals


Exercises



Mathematics



Matrix Algebra



Optimization



Statistics



Estimation



Tests of Hypotheses and Confidence Regions



Statistical Tables



Data Tables


References


Index