Description

Book Synopsis


Table of Contents
  • I. GETTING STARTED.
  • 1. Aspects of Multivariate Analysis.
  • 2. Matrix Algebra and Random Vectors.
  • 3. Sample Geometry and Random Sampling.
  • 4. The Multivariate Normal Distribution.
  • II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
  • 5. Inferences About a Mean Vector.
  • 6. Comparisons of Several Multivariate Means.
  • 7. Multivariate Linear Regression Models.
  • III. ANALYSIS OF A COVARIANCE STRUCTURE.
  • 8. Principal Components.
  • 9. Factor Analysis and Inference for Structured Covariance Matrices.
  • 10. Canonical Correlation Analysis
  • IV. CLASSIFICATION AND GROUPING TECHNIQUES.
  • 11. Discrimination and Classification.
  • 12. Clustering, Distance Methods and Ordination.
  • Appendix.
  • Data Index.
  • Subject Index.

Applied Multivariate Statistical Analysis Classic

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    Order before 4pm today for delivery by Tue 23 Jun 2026.

    A Paperback by Richard A. Johnson, Dean Wichern

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      View other formats and editions of Applied Multivariate Statistical Analysis Classic by Richard A. Johnson

      Publisher: Pearson Education
      Publication Date: 3/18/2018 12:00:00 AM
      ISBN13: 9780134995397, 978-0134995397
      ISBN10: 0134995392

      Description

      Book Synopsis


      Table of Contents
      • I. GETTING STARTED.
      • 1. Aspects of Multivariate Analysis.
      • 2. Matrix Algebra and Random Vectors.
      • 3. Sample Geometry and Random Sampling.
      • 4. The Multivariate Normal Distribution.
      • II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
      • 5. Inferences About a Mean Vector.
      • 6. Comparisons of Several Multivariate Means.
      • 7. Multivariate Linear Regression Models.
      • III. ANALYSIS OF A COVARIANCE STRUCTURE.
      • 8. Principal Components.
      • 9. Factor Analysis and Inference for Structured Covariance Matrices.
      • 10. Canonical Correlation Analysis
      • IV. CLASSIFICATION AND GROUPING TECHNIQUES.
      • 11. Discrimination and Classification.
      • 12. Clustering, Distance Methods and Ordination.
      • Appendix.
      • Data Index.
      • Subject Index.

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