Multivariable Modeling - Linear Regression

A set of flashcards defining key vocabulary related to linear regression and multivariable modeling concepts.

Linear Regression

A statistical method used to model the relationship between a dependent variable and one or more independent variables.

Dependent Variable (Y)

The outcome variable in a regression model that is predicted or explained.

Independent Variable (x)

A variable that is believed to influence or predict changes in the dependent variable.

Intercept (β0)

The expected value of the dependent variable when all independent variables are set to zero.

Slope (β)

The average change in the dependent variable for a one-unit change in the independent variable, holding other variables constant.

Error Term (ε)

The difference between the observed value and the predicted value; it represents unobserved factors affecting the dependent variable.

Assumptions of Linear Regression

Key conditions that must be satisfied for linear regression results to be valid, including linearity, independence, and normality of errors.

Collinearity

A situation in which two or more independent variables in a regression model are highly correlated, which can affect the stability of coefficient estimates.

Confidence Interval (CI)

A range of values that is likely to contain the true parameter value with a certain level of confidence, typically 95%.

PROC GENMOD

A SAS procedure used to fit generalized linear models, allowing for analysis of various types of outcome variables and complex models.