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.
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Cliff et al. (1999) studied this question.