Stacey, Brian (2015): Fukushima: The Failure of Predictive Models.

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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.
Item Type:  MPRA Paper 

Original Title:  Fukushima: The Failure of Predictive Models 
Language:  English 
Keywords:  Regression, Residual Errors, Prediction, Earthquake, Fukushima 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation 
Item ID:  69383 
Depositing User:  Brian Stacey 
Date Deposited:  10 Feb 2016 06:13 
Last Modified:  10 Feb 2016 07:43 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/69383 