- regression(double[][], double[]) - Method in class models.LeastSquare_Regression
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A default constructor for regression where the intercept (constant) is assumed to be included.
- regression(double[][], double[], boolean) - Method in class models.LeastSquare_Regression
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This the main method of this class that will compute the most
important OLS regression statistics such as the :
Beta
Anova
R and R-Squared
T-statistics for the predictors
Beat standard errors
T Pvalues and more
- regression(double[][], double[], boolean, double, int) - Method in class models.Logistic_Regression
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This is the main Newton-Raphson method for Logistic Regression
- regression(double[][], double[]) - Method in class models.Logistic_Regression
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A constructor of logistic regression for faster execution.
- regression(double[][], double[], double, int) - Method in class models.Logistic_Regression
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A constructor of logistic regression for faster execution, where we put an intercept
- regression(double[][], String[], double, int) - Method in class models.Multinomial_Logistic_Regression
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This is the main method that will compute the coefficients and all other stats for the Multinomial Logistic model.
- regression(double[][], String[]) - Method in class models.Multinomial_Logistic_Regression
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A simpler constructor of Multinomial Logistic Regression.
- removeElement(int) - Method in class Assisted_Classes.DescriptiveStatistics
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This method removes an element from the current instance of DescriptiveStatistics