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Weather Variables Significance Prediction Using Logistic Regression

This study demonstrates the significance of weather variables by utilizing logistic regression with p-levels above 95% confidence and cross-validation with a Brier score cost function. The logistic regression method includes step-wise forward selection and is applied to predict the probability of occurrence in weather-related cases or epidemics.

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Weather Variables Significance Prediction Using Logistic Regression

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  1. Significant Weather Variable terms … Significance shown using logistic regression with both: 1) plevels at greater than 95% group confidence (99.94%); and 2) cross-validation with a Brier score cost function

  2. Fitting all Weather Variables together … Step-wise forward selection used logistic regression and cross-validation with Brier score cost function

  3. Logistic Regression for probability of occurrence (“any case” or “epidemic 15/105 )

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