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.
Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures including t -tests, analysis of variance, and multiple regression to analogous multivariate techniques that involve several dependent variables.
The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space.
In addition, the authors explore a wealth of newly added topics, including:. New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.
View Student Companion Site. He has been published extensively in his areas of research interest, which include multivariate analysis, resampling methods, and spatial and environmental statistics.
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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.
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Write a review Rate this item: Preview this item Preview this item. Applied multivariate statistical analysis Author: Harlow, Essex, England: Sixth edition View all editions and formats Rating: Subjects Multivariate analysis. Multivariate analysis More like this Similar Items. Allow this favorite library to be seen by others Keep this favorite library private. Find a copy in the library Finding libraries that hold this item Details Material Type: Document, Internet resource Document Type: Reviews User-contributed reviews Add a review and share your thoughts with other readers.
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Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Final Comments.
Sample Geometry and Random Sampling. The Geometry of the Sample. Generalized Variance. Sample Values of Linear Combinations of Variables. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra.
Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices.
Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. The Multivariate Normal Distribution. Assessing the Assumption of Normality. 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.