Richard A. Johnson, Dean W. Wichern

Applied Multivariate Statistical Analysis (eBook, PDF)

Pearson New International Edition

• Format: PDF

Produktbeschreibung

For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics.

Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analysing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analysing multivariate data, the text assumes two or more statistics courses as a prerequisite.

The full text downloaded to your computer

With eBooks you can:

• search for key concepts, words and phrases
• make highlights and notes as you study
• share your notes with friends

eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps.

Upon purchase, you'll gain instant access to this eBook.

Time limit

The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Pearson HigherEducation
• Seitenzahl: 776
• Altersempfehlung: ab 18 Jahre
• Erscheinungstermin: 29. August 2013
• Englisch
• ISBN-13: 9781292037578
• Artikelnr.: 41940370

Autorenporträt

Dean W. Wichern is Professor Emeritus at the Mays School of Business at Texas A&M University. He holds membership in the American Statistical Association, Royal Statistical Society, International Institute of Forecasters, and Institute for Operations Research and the Management Sciences. He is the author for four textbooks and was Associate Editor of Journal of Business and Economic Statistics from 1983-1991.

Professor Richard A. Johnson is Professor in the Department of Statistics at the University of Wisconsin. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association and he is amember of the Royal Statistical Society and International Statistical Institute. He is the author of six textbooks and over 120 technical publications and is the founding Editor of Statistics and Probability Letters (1981-).

Inhaltsangabe

DRAFT
 
(NOTE: Each chapter begins with an Introduction, and concludes with
Exercises and References.)
I. GETTING STARTED.
1. Aspects of Multivariate Analysis.
 
Applications of Multivariate Techniques. The Organization of Data. Data
Displays and Pictorial Representations. Distance. Final Comments.
 
 
2. Sample Geometry and Random Sampling.
 
The Geometry of the Sample. Random Samples and the Expected Values of the
Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean,
Covariance, and Correlation as Matrix Operations. Sample Values of Linear
Combinations of Variables.
3. 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.  
 
4. The Multivariate Normal Distribution.
 
The Multivariate Normal Density and Its Properties. Sampling from a
Multivariate Normal Distribution and Maximum Likelihood Estimation. The
Sampling Distribution of `X and S. Large-Sample Behavior of `X and S.
Assessing the Assumption of Normality. Detecting Outliners and Data
Cleaning. Transformations to Near Normality.
 
II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
5. Inferences About a Mean Vector.
 
The Plausibility of …m0 as a Value for a Normal Population Mean.
Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and
Simultaneous Comparisons of Component Means. Large Sample Inferences about
a Population Mean Vector. Multivariate Quality Control Charts. Inferences
about Mean Vectors When Some Observations Are Missing. Difficulties Due To
Time Dependence in Multivariate Observations. Supplement 5A Simultaneous
Confidence Intervals and Ellipses as Shadows of the p-Dimensional
Ellipsoids.
 
6. Comparisons of Several Multivariate Means.
 
Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors
from Two Populations. Comparison of Several Multivariate Population Means
(One-Way MANOVA). 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.
 
7. 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. Comparing the Two
Formulations of the Regression Model. Multiple Regression Models with Time
Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio
for the Multivariate Regression Model.
 
III. ANALYSIS OF A COVARIANCE STRUCTURE.
8. Principal Components.
 
Population Principal Components. Summarizing Sample Variation by Principal
Components. Graphing the Principal Components. Large-Sample Inferences.
Monitoring Quality with Principal Components. Supplement 8A The Geometry of
the Sample Principal Component Approximation.
 
9. Factor Analysis and Inference for Structured Covariance Matrices.
 
The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor
Scores. Perspectives and a Strategy for Factor Analysis. Structural
Equation Models. Supplement 9A Some Computational Details for Maximum
Likelihood Estimation.
 
10. Canonical Correlation Analysis
 
Canonical Variates and Canonica