Prediction Revisited. Mark P. Kritzman

      

      Table of Contents

      1  Cover

      2  Title Page

      3  Copyright

      4  Timeline of Innovations

      5  Essential Concepts

      6  Preface

      7  1 Introduction Relevance Roadmap Note

      8  2 Observing Information Observing Information Conceptually Observing Information Mathematically Observing Information Applied Appendix 2.1: On the Inflection Point of the Normal Distribution References Notes

      9  3 Co-occurrence Co-occurrence Conceptually Co-occurrence Mathematically Co-occurrence Applied References Note

      10  4 Relevance Relevance Conceptually Relevance Mathematically Relevance Applied Appendix 4.1: Predicting Binary Outcomes References Notes

      11  5 Fit Fit Conceptually Fit Mathematically Fit Applied Notes

      12  6 Reliability Reliability Conceptually Reliability Mathematically Reliability Applied References Notes

      13  7 Toward Complexity Toward Complexity Conceptually Toward Complexity Mathematically Complexity Applied References

      14  8 Foundations of Relevance Observations and Relevance: A Brief Review of the Main Insights Abraham de Moivre (1667–1754) Pierre-Simon Laplace (1749–1827) Carl Friedrich Gauss (1777–1853) Francis Galton (1822–1911) Karl Pearson (1857–1936) Ronald Fisher (1890–1962) Prasanta Chandra Mahalanobis (1893–1972) Claude Shannon (1916–2001) References Notes

      15  Concluding Thoughts Perspective Insights Prescriptions

      16  Index

      17  End User License Agreement

      List of Tables

      1 Chapter 2Exhibit 2.5 DatasetExhibit 2.6 Arithmetic AveragesExhibit 2.9 Pairwise Spreads and Variance Calculation—Industrial Production...Exhibit 2.10 Conventional Variance Calculation—Industrial Production...Exhibit 2.11 Arithmetic Averages, Variances, and Standard DeviationsExhibit 2.12 Flipping 10 Coins

      2 Chapter 3Exhibit 3.4 Correlation MatrixExhibit 3.6 Pairwise z-scores for Individual AttributesExhibit 3.7 Pairwise Co-occurrence—Industrial Production and