# 
## Overview

In a linear function, **simple linear regression** defines the connection between an independent variable x and a dependent variable y.

```
y = slope * x + intercept
```

> **Note**: Refer to [A deep dive into linear regression](https://www.educative.io/answers/a-deep-dive-into-linear-regression-3-way-implementation) to learn more about linear regression.

The **`linear_regression()` method** is used to get the `slope` and `intercept` of a simple linear regression given the values `x` and `y` using the ordinary least squares estimation method. This method was introduced in Python version 3.10.

The inputs `x` and `y` should be the same length and should at least have two data points.

# 
## Syntax

```
linear_regression(x, y)
```

## Parameters

- **`x`**: This is the independent variable.
- **`y`**: This is the dependent variable.

## Return value

The method returns the `slope` and `intercept`.


## Code
Let's look at the code below:

import statistics

x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [11 ,13, 12, 15, 17, 18, 18, 19, 20, 22]

slope, intercept = statistics.linear_regression(x, y)

print("Slope - ", slope)
print("Intercept - ", intercept)

python39

# 
## Explanation

- **Line 1**: We import the `statistics` module.
- **Line 3**: We define the independent variable `x`.
- **Line 4**: We define the dependent variable `y`.
- **Line 5**: We obtain the `slope` and `intercept` of `x` and `y` using the  `linear_regression()`method.
- **Lines 8 and 9**: We print the `slope` and `intercept` value.

python310.tar.gz

What is the statistics.linear_regression() method in Python?

Defines relationships between variables, find slope and intercept using ordinary least squares in Python 3.10+ with linear_regression() method.