Linear regression tries to fit a line through a given set of points, choosing the best fit. The best fit is the line that minimizes the summed squared difference between the value dictated by the line for a certain value of *x* and its corresponding *y* values. (It is optimizing the same squared error that we met before when checking how good a mean was as a predictor.)

Since linear regression is a line; in bi-dimensional space (*x*, *y*), it takes the form of the classical formula of a line in a Cartesian plane: *y = mx + q*, where *m* is the angular coefficient (expressing the angle between the line and the *x* axis) and *q* is the intercept between the line and the *x* axis.

Formally, machine learning indicates the correct expression ...

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