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The least squares criterion is used to find an equation (usually linear) that minimizes the sum of squared differences between observed and predicted values.
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Residuals: Residuals are the differences between observed values and predicted values in a regression analysis.
Regression Line: The regression line is the line that best fits a set of data points using the least squares criterion.
Coefficient of Determination (R-squared): R-squared measures how well a regression model fits the data, ranging from 0 to 1.