2. Regression Flashcards
Simple linear Regression
Predicting a quantitative response variable from a quantitative explanatory variable.
Polynomial Regression
Predicting a quantitative response variable from a quantitative explanatory variable, where the relationship is modeled as an nth order polynomial.
Multiple linear Regression
Predicting a quantitative response variable from two or more explanatory variables.
Multilevel Regression
Predicting a response variable from data that have a hierarchical structure (for example, students within classrooms within schools). Also called hierarchical, nested, or mixed models.
Multivariate Regression
Predicting more than one response variable from one or more explanatory variables.
Logistic Regression
Predicting a categorical response variable from one or more explanatory variables.
Poisson Regression
Predicting a response variable representing counts from one or more explanatory variables.
Cox proportional hazards Regression
Predicting time to an event (death, failure, relapse) from one or more explanatory variables.
Time-series Regression
Modeling time-series data with correlated errors.
Nonlinear Regression
Predicting a quantitative response variable from one or more explanatory variables, where the form of the model is nonlinear.
Nonparametric Regression
Predicting a quantitative response variable from one or more explanatory variables, where the form of the model is derived from the data and not specified a priori.
Robust Regression
Predicting a quantitative response variable from one or more explanatory variables using an approach that’s resistant to the effect of influential observations.
Which types of Regression fall under Ordinary-Least Square Regression?
linear regression, polynomial regression, and multiple linear regression.
OLS Regression
In OLS regression, a quantitative dependent variable is predicted from a weighted
sum of predictor variables, where the weights are parameters estimated from the data.
