Using Linear Regression to Predict Scores

These flashcards cover key vocabulary and concepts related to linear regression and prediction, based on the provided lecture notes.

Linear Regression

A statistical method for modeling the relationship between a dependent variable and one or more independent variables.

Regression Equation

An equation that describes the relationship between variables, typically in the form of Y = a + bX.

Standard Error of the Estimate

A measure that quantifies the accuracy of predictions made with a regression line by calculating the average distance between predicted values and actual values.

Predictable Variance

The portion of variance in the dependent variable that can be explained by the independent variable(s), often expressed as a percentage.

Outliers

Data points that deviate significantly from the overall pattern of the data, potentially distorting the results of regression analysis.

Criterion Variable

The variable that is predicted or explained in a regression analysis; also known as the dependent variable.

Predictor Variable

The variable used to make predictions about the criterion variable; also known as the independent variable.

Best-Fitting Line

The line that minimizes the distance (error) between the predicted values and the actual data points in a regression analysis.

Covariance

A measure of the degree to which two variables change together, which is used in calculating regression coefficients.

Residuals

The differences between observed values and the values predicted by a regression model.