# Linear regression calculator

Two-dimensional linear regression of statistical data is done by the**method of least squares**. Enter the statistical data in the form of a pair of numbers, each pair is on a separate line. The first number is considered as X (each odd-numbered in the order), second as Y (each even-numbered in the order).

The output of the linear regression is coefficients

**A**and

**B**of the linear function f(x) = Ax + B, which approximates given 2D data by linear function (line). Least squares means that we minimize the sum of the squares of the errors made in the results of every point.

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**coefficient of correlation**Pearson product-moment correlation coefficient (PPMCC or PCC or R) is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation.

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