Multiple Linear Regression: Key Concepts and Terminology

These flashcards capture essential vocabulary and concepts central to understanding multiple linear regression and its application in modeling relationships in data.

Multiple Regression Analysis

A statistical technique that models the relationship between a dependent variable and multiple independent variables.

Dependent Variable

The variable being predicted or explained in a regression analysis.

Independent Variable

A variable that is hypothesized to influence or predict the dependent variable.

Dummy Variable

A variable assigned a value of 0 or 1 to represent the presence or absence of a characteristic in regression analysis.

Model Specification

The process of defining the dependent variable, selecting independent variables, and obtaining the sample data.

Coefficient of Determination (R²)

A measure that indicates the proportion of the variance in the dependent variable that is explained by the independent variables in the regression model.

Standard Error

A measure of the accuracy of predictions made with a regression model, reflecting the average distance that the observed values fall from the regression line.

Multicollinearity

A situation in regression analysis where two or more independent variables are highly correlated, leading to redundancy in information.

Nonlinear Relationships

Relationships between variables that do not follow a straight line, requiring transformations or polynomial terms in regression analysis.

Model Diagnosis

The process of evaluating the regression model for validity, checking for adherence to statistical assumptions.