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==Classification of Regression models==
==Binary logistic regression==
===Conversion of binary logistic regression equation logit of outcome odds to outcome probability <math>p</math>===
Equation of binary logistic regression can be converted to outcome probablity <math>p</math> as,
:<math>
</math>
===How to convert Conversion of coefficient of binary logistic regressoin to odds ratio===
When thinking about outcome probability <math>p</math> and the changed outcome probability <math>p\prime</math> by adding <math>1</math> to explanatory variable <math>X_1</math>, the following two equations are obtained,
:<math>
Thus, converted <math>b_1</math> to <math>\color{red}{e^{b_1}}</math> or <math>\color{red}{\exp (b_1)}</math> gives the '''odds ratio of outocome probabilities''' before and after variable <math>X_1</math> gains <math>1</math>.
::<math>
\begin{align}
\exp (\text{coefficient}) = e^{\text{coefficient}} & = \text{odds ratio} \\
\log (\text{odds ratio}) & = \text{coefficient}
\end{align}
</math>
==Generalized linear model==
