Multiple Regression Analysis: Estimation

This set of flashcards covers key vocabulary terms and concepts from the lecture on Multiple Regression Analysis, focusing on definitions and critical elements essential for understanding econometric methods.

Multiple Linear Regression Model

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

Ceteris Paribus

A Latin phrase meaning 'all other things being equal'; used in economics to analyze the effect of one variable while holding others constant.

Ordinary Least Squares (OLS)

A method for estimating the unknown parameters in a multiple linear regression model by minimizing the sum of squared differences between observed and predicted values.

Dependent Variable

The outcome variable that the model aims to predict or explain, commonly denoted as y.

Independent Variables

The predictors or explanatory factors in a regression model, usually denoted as x1, x2, …, xk.

Residual

The difference between the observed value and the predicted value of the dependent variable.

Goodness-of-Fit

A measure of how well the statistical model fits the data, often assessed using the coefficient of determination (R²).

Zero Conditional Mean Assumption

The assumption that the error term in a regression model is uncorrelated with the independent variables.

Overfitting

A modeling error that occurs when a model is too complex and captures noise in the data rather than the actual relationship.

Partialling Out

A technique used to control for the influence of one or more independent variables in order to isolate the effect of another variable.