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Fukushima: The Failure of Predictive Models

Author

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  • Stacey, Brian

Abstract

Linear regression is an attempt to build a model from existing data. It uses the existing data to fit a linear equation that exhibits the least error from the actual points. Even within the existing data, a regression equation has limited ability to predict actual dependent variable values. Linear regression is limited in several ways; it assumes only linear relationships between variables, it is very sensitive to outliers, and data points must be independent. If any of these assumptions are not met the models get less accurate. Often random error (residuals) are enough to reduce the quality of fit of the equation to where it is unable to predict any values within the range of the original data except (x ̅,y ̅). Even with the best coefficient of determination (R2=1), data within the sample set may not be indicative of the condition outside the sample set.

Suggested Citation

  • Stacey, Brian, 2015. "Fukushima: The Failure of Predictive Models," MPRA Paper 69383, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:69383
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    More about this item

    Keywords

    Regression; Residual Errors; Prediction; Earthquake; Fukushima;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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