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Chapter One. Overview of Multivariate Methods.
1.0 Introduction.
1.1 Multivariate Methods as an Extension of Familiar Univariate Methods.
1.2 Measurement Scales and Data Types.
1.3 Four Basic Dataset Structures for Multivariate Analysis.
1.4 Pictorial Overview of Multivariate Methods.
1.5 Correlational versus Experimental Methods.
1.6 Old Versus New Methods.
1.7 Summary.
Chapter 2. The Seven Habits of High Effective Quants: A Review of Elementary Statistics Using Matrix Algebra.
2.0 Introduction.
2.1 The Meaning of Measurement Scales.
2.2 The Meaning of Measures of Central Tendency.
2.3 Variance and Matrix Algebra.
2.4 Covariance Matrices and Correlation Matrices.
2.5 Classical Probability Theory and the Binomial: The Basis for Statistical Inference.
2.6 Significance Tests: From Binomial to z Tests to t Tests to Analysis of Variance.
2.7 Matrix Approach to Analysis of Variance.
2.8 Summary.
Chapter 3. Fundamentals of Matrix Algebra.
3.0 Introduction.
3.1 Definitions and Notation.
3.2 Matrix Operations and Statistical Quantities.
3.3 Partitioned Matrices and Adjoined Matrices.
3.4 Triangular Square Root Matrices.
3.5 Determinants.
3.7 Matrix Inversion.
3.7 Rank of a Matrix.
3.8 Orthogonal Vectors and Matrices.
3.9 Bilinear Forms and Quadratic Forms.
3.10 Eigenvectors and Eigenvalues.
3.11 Spectral Decomposition and Triangular Decomposition.
3.12 Normalization of a Vector.
Chapter Four. Factor Analysis and Related Methods: Quintessentially Multivariate.
4.0 Introduction.
4.1 An Applied Example of Factoring: The Mental Skills of Mice.
4.2 Calculating Factor Loadings to Reveal the Structure of Skills in Mice.
4.3 Simplest Case Mathematical Demonstration of a Complete Factor Analysis.
4.4 Factor Scores: The Relationship between Latent Variables and Manifest Variables.
4.5 Principal Component Analysis: Simplified Factoring of Covariance Structure.
4.6 Rotation of the Factor Pattern.
4.7 The Rich Variety of Factor Analysis Models.
4.8 Factor Analyzing the Mental Skills of Mice: A Comparison of Factor Analytic Models.
4.9 Data Reliability and Factor Analysis.
4.10 Summary.
Chapter Five. Multivariate Graphics.
5.0 Introduction.
5.1 Latour’s Graphicity Thesis.
5.2 Nineteenth Century Male Names: The Construction of Convergent Multivariate Graphs.
5.3 Varieties of Multivariate Graphs.
5.4 Flourishing Families: An Illustration of Linked Graphics and Statistical Analyses in Data Exploration.
5.5 Summary.
Chapter Six. Canonical Correlation: The Underused Method.
6.0 Introduction.
6.1 An Applied Example of Canonical Correlation: Personality Orientations and Prejudice.
6.2 Mathematical Demonstration of a Complete Canonical Correlation Analysis.
6.3 Illustration of Canonical Correlation Tables and Graphics with Finance Data.
6.4 Canonical Correlation as a Filter for Multiple Regression.
6.5 Summary and Conclusions.
Chapter Seven. Hotelling’s T-Squared as the Simplest Case of Multivariate Inference.
7.0 Introduction.
7.1 An Applied Example of Hotelling’s T-Squared: Family Finances and Relational Aggression.
7.2 Multivariate Versus Univariate Significance Tests.
7.3 The Two Sample Independent Groups Hotelling’s T-Squared Test.
7.4 Discriminant Analysis from a Hotelling’s T-Squared Test.
7.6 Summary and Conclusions.
Chapter 8. Multivariate Analysis of Variance.
8.0 Introduction.
8.1 An Applied Example of Multivariate Analysis of Variance (MAVI).
8.2 One-Way Multivariate Analysis of Variance (MAVI).
8.3 The Four Multivariate Significance Tests.
8.4 Summary and Conclusions.
Chapter Nine. Multiple Regression.
9.0 Introduction.
9.1 The Fundamental Method of Multiple Regression.
9.2 Summary and Conclusions.
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Add Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach, Multivariate Analysis for the Social Sciences provides clear guidelines combined with the insight needed to understand the methods and applications of multivariate statistics. This easy-to-follow book provides students in social, behavioral, and he, Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach to the inventory that you are selling on WonderClubX
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Add Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach, Multivariate Analysis for the Social Sciences provides clear guidelines combined with the insight needed to understand the methods and applications of multivariate statistics. This easy-to-follow book provides students in social, behavioral, and he, Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach to your collection on WonderClub |