Prediction Revisited - Mark P. Kritzman

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Co-occurrence—Industrial Produ...Exhibit 3.9 Pairwise Correlation Calculation—Industrial Production and Nonf...Exhibit 3.11 Conventional Correlation Calculation—Industrial Production and...Exhibit 3.12 Correlation MatrixExhibit 3.13 Covariance Matrix

      3 Chapter 4Exhibit 4.9 Observations and Their Relevance to

Exhibit 4.11 Relevance-Weighted Outcomes and Full-Sample Regression Predict...Exhibit 4.13 Conventional Full-Sample Regression PredictionExhibit 4.15 Partial Sample Prediction CalculationExhibit 4.16 Asymmetry Calculation for
PredictionExhibit 4.17 Aggregate Asymmetry Calculation

      4 Chapter 5Exhibit 5.3 Pairwise Relevance and OutcomesExhibit 5.4 Prediction FitExhibit 5.5 Prediction Variance and BoundsExhibit 5.6 PrecisionExhibit 5.7 Calculating Prediction Fit for Partial Sample PredictionExhibit 5.8 Prediction Variance, Bounds, and Precision for Partial Sample P...

      5 Chapter 6Exhibit 6.3 Calculating ReliabilityExhibit 6.4 Pairwise Relevance and Pairwise OutcomesExhibit 6.5 Pairwise Calculation of Full-Sample ReliabilityExhibit 6.6 Traditional Approach to Calculating R-squaredExhibit 6.7 Calculating Reliability for Partial Sample RegressionExhibit 6.9 Removing the Impact of Biased TermsExhibit 6.10 Addressing the Bias of R-Squared

      6 Chapter 8Exhibit 8.1 Binomial Distribution

      List of Illustrations

      1 Chapter 2Exhibit 2.1 SpreadsExhibit 2.2 Triangle of PairsExhibit 2.3 Subsequent GDP GrowthExhibit 2.4 Trailing Percentage Changes in Industrial ProductionExhibit 2.7 Industrial ProductionExhibit 2.8 Histogram of Industrial ProductionExhibit 2.13 Distribution of CombinationsExhibit 2.14 Counting Distinct Combinations for 30 Trials

      2 Chapter 3Exhibit 3.1 Two Positively Related Attributes for a Single ObservationExhibit 3.2 Possible Co-occurrence Patterns for Attributes with a Given Aver...Exhibit 3.3 Co-occurrence for Single ObservationsExhibit 3.5 Pairs of Pairs Approach for Estimating Co-occurrenceExhibit 3.10 Pairwise Co-occurrence—Industrial Production and Nonfarm Payrol...

      3 Chapter 4Exhibit 4.1 Scatter Plot of Two Hypothetical AttributesExhibit 4.2 Similarity, Informativeness, and Relevance of Hypothetical Obser...Exhibit 4.3 Simulated Symmetric RelationshipExhibit 4.4 Predictions versus Actual OutcomesExhibit 4.5 Simulated Asymmetric RelationshipExhibit 4.6 Predictions versus Actual OutcomesExhibit 4.7 Informativeness and Similarity with Two AttributesExhibit 4.8 Equally Informative Observations with Five AttributesExhibit 4.10 Relevance of Observations to

Exhibit 4.12 Outcome Deviations from AverageExhibit 4.14 Relevance and Outcome Deviations—25% Most Relevant Observations...Exhibit 4.18 AsymmetryExhibit 4.19 Logistic FunctionExhibit 4.20 Logit Function (inverse of the logistic function)

      4 Chapter 5Exhibit 5.1 Relevance and Outcomes for a Very Good Partial FitExhibit 5.2 Pairwise Relevance and Fit

      5 Chapter 6Exhibit 6.1 Components of Fit for Simulated Random NoiseExhibit 6.2 Components of Fit for Simulated Random NoiseExhibit 6.8 Reliability of Partial Sample Regression

      6 Chapter 7Exhibit 7.1 Decision Tree

      7 Chapter 8Exhibit 8.2 Galton's Quincunx

      Guide

      1  Cover

      2  Table of Contents

      3  Title Page

      4  Copyright

      5  Timeline of Innovations

      6  Essential Concepts

      7  Preface

      8  Begin Reading

      9  Concluding Thoughts

      10  Index

      11  End User License Agreement

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