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 Johnson, Richard A. (Richard Arnold), 1937 author.
 Sixth edition.  [New York, NY] : Pearson Education, Inc., [2019]
 Description
 Book — xviii, 773 pages ; 24 cm.
 Summary

For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduatelevel courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this marketleading 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 analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
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QA278 .J63 2019  Unknown 
2. Statistics : principles and methods [2014]
 Johnson, Richard A. (Richard Arnold), 1937
 7th ed.  Hoboken, N.J. : Wiley, c2014.
 Description
 Book — xv, 716 pages : ill ; 24 cm
 Summary

Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include realworld data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods. New What Will We Learn opening paragraphs set the stage for the material being discussed. Using Statistics Wisely boxes summarize key lessons. In addition, Statistics in Context sections give business professionals an understanding of applications in which a statistical approach to variation is needed.
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QA276.12 .J63 2014  Unknown 
 Johnson, Richard A. (Richard Arnold), 1937
 6th ed.  Upper Saddle River, N.J. : Pearson Prentice Hall, c2007.
 Description
 Book — xviii, 773 p. : ill. ; 25 cm.
 Summary

 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. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A SquareRoot Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts.
 3. 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.
 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. LargeSample 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 pDimensional 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 (OneWay MANOVA). Simultaneous Confidence Intervals for Treatment Effects. TwoWay 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. LargeSample 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 Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES.
 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments.
 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's tDistribution Percentage Points. ...c2 Distribution Percentage Points. FDistribution Percentage Points. FDistribution Percentage Points (...a = .10). FDistribution Percentage Points (...a = .05). FDistribution Percentage Points (...a = .01). Data Index. Subject Index.
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QA278 .J63 2007  Unknown 
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