Python statistics.linear_regression() Function

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The Python statistics.linear_regression() function returns the slope and intercept of simple linear parameters using least squares.

Simple linear regression describes the relationship between an independent variable x and y in terms of the linear function i.e., represented as:

y = slope^*x + intercept + noice

Here, slope and intercepts are the regression parameters that are estimated and noice represents the variability of the data that was given in the linear regression. These values are equal to the difference between predicted and actual values of the variables.

Input variables must be in the same length and the independent variable x cannot be a constant; otherwise this throws StatisticsError.

Syntax

Following is the basic syntax for the statistics.linear_regression() function.

statistics.linear_regression(x, y, /, *, proportional = False)

Parameters

This function takes x and y values whereas, x is independent and y is dependent variable.

Return Value

Regression function returns the slope and intercept.

Example 1

Here, we are demonstrating statistics.linear_regression() function to determine the x and y values.

import statistics
x = [0, 1, 2, 3, 4, 5, 6, 7]
y = [12, 13, 12, 15, 16, 17, 18, 19]
slope, intercept = statistics.linear_regression(x, y)
print("slope - ", slope)
print("Intercept - ", intercept)

Output

The result is produced as follows −

slope -  1.0714285714285714
Intercept -  11.5

Example 2

In the below example we are calculating negative numbers using statistics.linear_regression() function.

import statistics
x = [-3, -4, -5, -7, -9, -4, -2]
y = [-13, -31, -14, -56, -35, -43, -23]
slope, intercept = statistics.linear_regression(x, y)
print("slope - ", slope)
print("Intercept -", intercept)

Output

We will get the output as follows −

slope -  3.262295081967214
Intercept - -14.868852459016392

Example 3

In the following example we are predicting the cumulative number of Monty Python films that would have been produced by 2019 assuming that they had kept the peace.

import statistics
x = [1971, 1975, 1979, 1982, 1983]
y = [1, 2, 3, 4, 5]
slope, intercept = statistics.linear_regression(x, y)
print(round(slope * 2019 + intercept))

Output

This will produce the following result −

16

Example 4

Here, we are calculating the linear regression of the given values (asuming x =100).

import statistics
x = [19, 75, 97, 82, 31]
y = [21, 12, 43, 74, 35]
slope, intercept = statistics.linear_regression(x, y)
print(round(slope * 100 + intercept))

Output

The output is obtained as follows −

49