Multivariate statistical analysis for geographers
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Description
Explains how to implement, interpret, and conduct diagnostics on the results of multivariate techniques. The book focuses on geo-referenced data analysis applications, with explicit diagnostics for the role played by spatial autocorrelation in multivariate analyses. It also aims to establish specific connections between popular spatial analysis and multivariate procedures, and outlines methodology for implementing spatial auto, logistic, and Poisson regressions.
Table of Contents
I. INTRODUCTION AND REVIEW. 1. Elementary Statistics Background. 2. Information Content in Geo-Referenced Data. 3. Introduction to Matrix Algebra. 4. Multiple Linear Regression Analysis and Correlation Analysis. II. INSTANCES OF THE GENERAL LINEAR MODEL. 5. Multivariate Analysis of Variance. 6. Principal Components and Factor Analysis. 7. Discriminant Function Analysis. 8. Cluster Analysis. 9. Canonical Correlation Analysis. III. NONLINEAR AND CATEGORICAL DATA MODELING. 10. Nonlinear Regression Analysis. 11. Spatial Autoregressive Analysis. 12. Special Nonlinear Regression Applications in Spatial Analysis. Epilogue. Appendices. Index.
Publication Information
Prentice Hall, Upper Saddle River, N.J., 1997
ISBN
9780136056928
Keywords
Statistical Analysis
Recommended Citation
Griffith, D. A.
, Amrhein, C.
(Eds.).
(1997). Multivariate statistical analysis for geographers, p. 345.
Available at:
https://ecommons.aku.edu/books/65