Multiple Regression Analysis Lecture

These flashcards cover key concepts and vocabulary from the lecture on Multiple Regression Analysis.

Multiple Regression

Analyzes the linear relationship between a dependent variable and multiple independent variables.

Dummy Variable

A nominal variable that can take on either 0 or 1, used in regression models for categorical data.

Collinearity

A situation where two or more independent variables in a regression model are highly correlated.

Reference Group

The omitted category in dummy variable regression analysis used for comparison.

Dependent Variable

The outcome or response variable that the model aims to predict.

Independent Variables

Factors or predictors that are used to explain variations in the dependent variable.

R-squared (R²)

A statistical measure that represents the proportion of variance for the dependent variable that's explained by the independent variables.

F-ratio

A ratio used to assess the overall significance of a regression model.

Multicollinearity

A condition in which independent variables in a regression model are correlated, leading to unreliable coefficient estimates.

VIF (Variance Inflation Factor)

A measure that checks for multicollinearity; a VIF greater than 2 indicates a potential problem.

Beta Coefficient

A value that represents the degree of change in the dependent variable for every one-unit change in the independent variable.

Significance Level (p-value)

The probability of obtaining a statistic as extreme as the observed one, under the null hypothesis; used to determine statistical significance.