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Ordinary least squares (OLS) is a statistical method that fits a linear regression line to a dataset. With OLS, you do this by squaring the vertical distance values that describe the distances between the data points and the best-fit line, adding up those squared distances, and then adjusting the placement of the best-fit line so that the summed squared distance value is minimized. Use OLS if you want to construct a function that’s a close approximation to your data.

As always, don’t expect the actual value to be identical to the value predicted by the regression. Values predicted by the regression are simply estimates that are most similar to the actual values in the model.

OLS is particularly useful for fitting a regression line to models containing more than one independent variable. In this way, you can use OLS to estimate the target from dataset features.

When using OLS regression methods to fit a regression line that has more than one independent variable, two or more of the variables may be interrelated. When two or more independent variables are strongly correlated with each other, this is called multicollinearity. Multicollinearity tends to adversely affect the reliability of the variables as predictors when they’re examined apart from one another. Luckily, however, multicollinearity doesn’t decrease the overall predictive reliability of the model when it’s considered collectively.