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)
		Monovariable (single variable)	Multivariable (multiple variables)
Dependent variable (outcome)	Continuous	Simple linear regression [math]\displaystyle{ Y = a + bX }[/math]	Multiple† 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	Multiple† 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	Multiple† multinominal logistic regression
	Ordinal	Simple ordinal logistic regression	Multiple† ordinal logistic regression
	Survival time		Multiple 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]

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