Read "An Introduction to Applied Multivariate Analysis" by Tenko Raykov available from Rakuten Kobo. Sign up today and get $5 off your first download. Methods of Multivariate Analysis. Second Edition. ALVIN C. RENCHER. Brigham Young University. A JOHN WILEY & SONS, INC. PUBLICATION. Editorial Reviews. Review. This text is very well written and makes important connections between univariate and multivariate procedures..[it] allows readers to.
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CHAPTER PREVIEW. This chapter presents a simplified overview of multivariate analysis. It stresses that multivariate analysis methods will increasingly. Praise for the Second Edition This book is a systematic, well-written, well- organized texton multivariate analysis packed with intuition and insight. Understand the nature of measurement error and its impact on multivariate analysis. • Determine which multivariate technique is appropriate for a specific.
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Methods of Multivariate Analysis, 3rd Edition. Selected type: Added to Your Shopping Cart. Christensen ISBN: Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor Analysis Classification Trees Dynamic Graphics Transformations to Normality Prediction for Multivariate Multiple Regression Kronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material.
Student View Student Companion Site. Permissions Request permission to reuse content from this site. Table of contents Preface xvii Acknowledgments xxi 1 Introduction 1 1. Detecting Outliners and Data Cleaning.
Transformations to Near Normality. Inferences About a Mean Vector. The Plausibility of Hotelling's T 2 and Likelihood Ratio Tests. Multivariate Quality Control Charts. Comparisons of Several Multivariate Means.
Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations.
Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis.
Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models.
Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model.
Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression.
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Comparing the Two Formulations of the Regression Model. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences.
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Monitoring Quality with Principal Components. The Orthogonal Factor Model. Methods of Estimation.
Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Interpreting the Population Canonical Variables. Additional Sample Descriptive Measures. Large Sample Inferences.
Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Clustering, Distance Methods and Ordination.
Methods of Multivariate Analysis, 3rd Edition
Similarity Measures. Designing Experiments and Analyzing Data.
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