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Applied Multivariate Methods for Data Analysts

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ISBN-10: 0534237967

ISBN-13: 9780534237967

Edition: 1998

Authors: Dallas E. Johnson

List price: $191.95
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Description:

Statisticians and nonstatisticians alike will appreciate this modern and comprehensive new text. Dallas Johnson uses real-life examples and explains the "when to," "why to," and "how to" of numerous multivariate methods, stressing the importance and practical application of each. He keeps technical details to a minimum for greater student understanding. Students will be able to DO multivariate analyses when they complete this book. Drawing on nearly 20 years of experience teaching public seminars and college courses in applied multivariate methods. Johnson emphasizes those aspects that have been most useful to practitioners trying to solve real problems using real data.
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Book details

List price: $191.95
Copyright year: 1998
Publisher: Brooks/Cole
Publication date: 2/6/1998
Binding: Hardcover
Pages: 425
Size: 7.75" wide x 9.50" long x 0.75" tall
Weight: 2.486
Language: English

Applied Multivariate Methods
An Overview of Multivariate Methods
Two Examples
Types of Variables
Data Matrices and Vectors
The Multivariate Normal Distribution
Statistical Computing
Multivariate Outliers
Multivariate Summary Statistics
Standardized Data and/or z-Scores
Exercises
Sample Correlations
Statistical Tests and Confidence Intervals
Summary
Exercises
Multivariate Data Plots
Three Dimensional Data Plots
Plots of Higher Dimensional Data
Plotting to Check for Multivariate Normality
Exercises
Eigenvalues and Eigenvectors
Trace and Determinant
Eigenvalues
Eigenvectors
Geometrical Descriptions (p=2)
Geometrical Descriptions (p=3)
Geometrical Descriptions (p>3)
Exercises
Principal Components Analysis
Reasons for Doing a PCA
Objectives of a PCA
PCA on the Variance-Covariance Matrix, Sigma
Estimation of Principal Components
Determining the Number of Principal Components
Caveats
PCA on the Correlation Matrix, P
Testing for Independence of the Original Variables
Structural Relationships
Statistical Computing Packages
Exercises
Factor Analysis
Objectives of an FA
Caveats
Some History on Factor Analysis
The Factor Analysis Model
Factor Analysis Equations
Solving the Factor Analysis Equations
Choosing the Appropriate Number of Factors
Computer Solutions of the Factor Analysis Equations
Rotating Factors
Oblique Rotation Methods
Factor Scores
Exercises
Discriminant Analysis
Discrimination for Two Multivariate Normal Populations
Cost Functions and Prior Probabilities (Two Populations)
A General Discriminant Rule (Two Populations)
Discriminant Rules (More than Two Populations)
Variable Selection Procedures
Canonical Discriminant Functions
Nearest Neighbor Discriminant Analysis
Classification Trees
Exercises
Logistic Regression Methods
Logic Regression Model
The Logit Transformation
Variable Selection Methods
Logistic Discriminant Analysis (More than Two Populations.)
Exercises
Cluster Analysis
Measures of Similarity and Dissimilarity
Graphical Aids in Clustering
Clustering Methods
Multidimensional Scaling
Exercises
Mean Vectors and Variance-Covariance Matrices
Inference Procedures for Variance-Covariance Matrices
Inference Procedures for a Mean Vector
Two Sample Procedures
Profile Analyses
Additional Two Groups Analyses
Exercises
Multivariate Analysis of Variance Manova
Dimensionality of the Alternative Hypothesis
Canonical Variates Analysis
Confidence Regions for Canonical Variates
Exercises
Prediction Models and Multivariate Regression
Multiple Regression
Canonical Correlation Analysis
Factor Analysis and Regression
Exercises
Matrix Results
Quadratic Forms
Eigenvalues and Eigenvectors
Distances and Angles
Miscellaneous Results
Work Attitudes Survey
Data File Structure
SPSS Data Entry Commands
SAS Data Entry Commands
Family Control Study
References