Multiple Regression Analysis: Further Topics

Vocabulary flashcards based on key concepts from the lecture on multiple regression analysis.

Multiple Regression Analysis

A statistical technique that uses several explanatory variables to predict the outcome of a response variable.

Adjusted R-squared

A modified version of R-squared that adjusts for the number of predictors in the model, useful for selecting between different models.

Residual Analysis

The examination of the differences between observed and predicted values to determine the accuracy of a model.

Logarithmic Functional Forms

Mathematical representations where one or more variables are transformed using logarithms to handle skewed data and interpret elasticity.

Quadratic Model

A model that includes polynomial terms up to the second degree, used to capture nonlinear relationships between variables.

Interaction Terms

Variables in a regression model that allow the effects of one variable to depend on the level of another variable.

Percentage Effects in Log Models

The interpretation of coefficients in log-transformed models to express changes in the dependent variable as percentages.

Goodness-of-Fit

A measure of how well a statistical model fits the data, commonly evaluated using R-squared.

Data Scaling

The process of adjusting values measured on different scales to a notionally common scale, important for comparing estimates.

Coefficients Interpretation

Understanding the meaning of the numerical values of coefficients in regression models, representing the relationship strength between variables.