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

The process of creating a model that forecasts future outcomes based on historical data.

The outcome variable that researchers are trying to predict or explain in a regression model.

A variable that is manipulated or categorized to observe its effect on the dependent variable.

A regression model that uses one independent variable to predict the dependent variable.

A regression model that uses two or more independent variables to predict the dependent variable.

The process of assessing a model's performance using unseen data to ensure its reliability.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

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

The difference between the observed value and the predicted value in a regression model.

The assumption that the residuals of a regression model are normally distributed.

A condition in regression analysis where the variance of residuals is not constant across all levels of the independent variable.

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

An observation that lies an abnormal distance from other values in a dataset, which can skew results.

The process of making sense of the coefficients and statistical outputs of a regression model.

The act of predicting future values based on past data patterns, often using regression models.

The condition of a dataset, which affects the reliability and validity of the analysis performed.

An approach to analyzing datasets to summarize their main characteristics, often using visual methods.

The process of selecting a subset of relevant features for use in model construction.

Quantitative measures used to assess how well a regression model performs, such as MAE and RMSE.

The graphical representation of data and model results to facilitate understanding and insights.

A process where colleagues evaluate each other's work to provide feedback and improve quality.

A comprehensive assignment that demonstrates students' mastery of regression analysis through practical application.

Using historical data to make informed predictions about future events or trends.

The examination of residuals to assess the fit of a regression model and identify potential issues.

The process of refining a regression model based on residual analysis and performance metrics.