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LSRL (Linear Regression)

Definition

LSRL, or Linear Regression, stands for Least Squares Regression Line. It is a line that best represents the relationship between two variables by minimizing the sum of squared differences between observed data points and predicted values on the line.

Analogy

Think of LSRL as a tailor-made suit. The tailor takes measurements from different people (data points) and creates a line that fits them all as closely as possible. The LSRL does the same by finding the best-fitting line for all the data points.

Related terms

Residuals: Residuals are the vertical distances between each observed data point and its corresponding predicted value on the LSRL. They measure how much each data point deviates from the regression line.

Extrapolation: Extrapolation refers to using a regression model to make predictions outside of the range of observed data. However, it can be risky because it assumes that relationships continue beyond what has been observed.

Correlation Coefficient (r): The correlation coefficient measures the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship.