Optimal Data Analysis: A Guidebook With Software for Windows
For individuals in the U.S. & U.S. territories
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Optimal Data Analysis: A Guidebook With Software for Windows is a powerful system for examining information and making predictions. Optimal data analysis (ODA) identifies a statistical model that yields the theoretical maximum possible level of predictive accuracy. Here is the first and only comprehensive exposition of the ODA paradigm and the first and only statistical software for conducting ODA analyses.
This powerful system is self-contained, stands alone, and provides users who have never heard of the ODA paradigm with all of the tools required to conduct analyses quickly and with a minimum of effort. The methodological infrastructure is explained conceptually, without requiring equations. A cornucopia of easy-to-understand examples—that alternative statistical packages fail to solve—are provided and illustrate how to conduct ODA in various contexts such as psychology, medicine, finance, biology, political science, geology, engineering, and ports.
The book discusses predictive analysis step-by-step using the ODA method, from defining the prediction goal and potential predictor variables, through evaluating model classification performance, statistical significance, and validity findings. Every example illustrates how to use the ODA software, and how to interpret and describe the program output.
This statistical software system will prove its usefulness and flexibility for researchers, practitioners, and analysts in all quantitative fields, and will become a new standard in predictive analysis.
- Introduction to the ODA paradigm (PDF, 163KB)
- Using the ODA software
- Evaluating Classification Performance
- Evaluating Statistical Significance
- Two-Category Class Variables
- Multicategory Class Variables
- Reliability Analysis
- Validity Analysis
- Optimizing Suboptimal Multivariable Models
- Multiple Sample Analysis
- Sequential Analyses
- Iterative Decomposition Analysis
Epilogue: The Future of ODA
Appendix A: Dunn and Sidak Adjusted Per-Comparison p
Appendix B: Troubleshooting: Common Problems and Their Possible Solutions
About the Authors