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Linear Regression (Least Squares Regression)

Definition

Linear regression is a statistical method used to model the relationship between two variables by fitting a linear equation to observed data points. It helps us understand how changes in one variable are associated with changes in another variable.

Analogy

Think of linear regression as finding the best-fitting line through a scatterplot of data points. Just like drawing a straight line that passes through most of the points, linear regression finds the line that best represents the overall trend in the data.

Related terms

Extrapolation: Extrapolation refers to using a mathematical model, such as linear regression, to estimate or predict values outside of an observed range based on patterns within that range. It assumes that trends observed within known data will continue beyond those limits.

Residuals: Residuals are the differences between observed data points and their corresponding predicted values from a statistical model, such as linear regression. They represent how well or poorly the model fits the data and provide insights into any systematic patterns or errors in predictions.

Slope: The slope in linear regression represents how much change occurs in the response variable for every one-unit increase in the predictor variable. It indicates both direction (positive or negative) and magnitude (steepness) of the relationship between variables.