Regression model

Basics & Definition
Epidemiology
Odds in statistics and Odds in a horse race
Collider bias
Data distribution
Statistical test
Regression model
Multivariate analysis
Marginal effects
Prediction and decision
Table-related commands in STATA
Missing data and imputation

Classification of Regression models

		Independent variable (exposure)
		Univariable (single variable)	Multivariable (multiple variables)
Dependent variable (outcome)	Continuous	Simple linear regression [math]\displaystyle{ Y = a + bX }[/math]	Multivariable† linear regression [math]\displaystyle{ Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math]
	Binary	Simple binary logistic regression [math]\displaystyle{ \log Y = a + bX }[/math] where [math]\displaystyle{ Y }[/math] is odds of outcome	Multivariable† binary logistic regression [math]\displaystyle{ \log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math] where [math]\displaystyle{ Y }[/math] is odds of outcome
	Multinominal ≥ 3	Simple multinominal logistic regression	Multivariable† multinominal logistic regression
	Ordinal	Simple ordinal logistic regression	Multivariable† ordinal logistic regression
	Rate ratio		Multivariable Poisson regression [math]\displaystyle{ \log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math] where [math]\displaystyle{ Y }[/math] is rate ratio
	Survival time		Multivariable proportional hazard regression = Cox hazard regression [math]\displaystyle{ \log h(T) = \log h_0(T) + b_1X_1 + b_2X_2 + b_3X_3 + \cdots }[/math] where [math]\displaystyle{ h(T) }[/math] is the hazard at time [math]\displaystyle{ T }[/math] and [math]\displaystyle{ h_0(T) }[/math] is the baseline hazard at time [math]\displaystyle{ T }[/math]

†'Multivariable' can be rephrased as 'Multiple'; Multivariable is NOT equal to 'Multivariate'!!