Least Squares Regression
Least Squares Regression is a statistical method used to find the line of best fit for a set of data points by minimizing the sum of the squared distances between the observed data points and the predicted values. It is commonly used in machine learning and data analysis to model the relationship between two variables. The method involves calculating the slope and intercept of the line that best fits the data, and then using these values to make predictions about future data points. The quality of the fit is measured by the coefficient of determination (R-squared), which ranges from 0 to 1 and represents the proportion of the variance in the dependent variable that is explained by the independent variable. A higher R-squared value indicates a better fit.
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