#   
## Overview 
The **`statistics` module** in Python comes with many statistical functions that help analyze numerical data. 

The **`statistics.correlation()` method** in Python is used to return Pearson's correlation coefficient between two inputs. 

The Pearson's correlation formula is:

Where: 

$r$ = The correlation coefficient. It is usually between -1 (negative correlation) and +1 (positive correlation). When the value is zero, it means that there is no correlation between the inputs. 

$x_{i}$= The values of the x dataset.

$x$ = The mean values of the x dataset.

$y_{i}$ = The values of the y dataset.

$y$ = The mean value of the y dataset.

## Syntax
```python
statisticcs.corrrelation(x,y,/)
```
## Parameters
The ``statistics.correlation()`` method takes the ``x`` and ``y`` parameters which represent the x and y values for which the correlation coefficient is to be determined.

## Return value 
The ``statistics.correlation()`` method returns the Pearson's correlation coefficient for two given inputs.

## Example
Let's use the ``statistics.correlation()`` method to determine the Pearson's correlation coefficient 
for two inputs, `x` and `y`:





import numpy as np
x = [11, 2, 7, 4, 15, 6, 10, 8, 9, 1, 11, 5, 13, 6, 15]
y = [2, 5, 17, 6, 10, 8, 13, 4, 6, 9, 11, 2, 5, 4, 7]

# to return the upper three quartiles
pearsons_coefficient = np.corrcoef(x, y)
print("The pearson's coeffient of the x and y inputs are: \n" ,pearsons_coefficient)


## Explanation
* **Line 1**: We import `numpy` as a module.
* **Lines  2 and 3**: We make two datasets, `x` and `y`.
* **Line 6**: We calculate the coefficient using the `np.corrcoef(x, y)` function and assign the result to a variable, `pearsons_coefficient`.

What is the statistics correlation() method in Python?

The statistics.correlation() method in Python calculates Pearson's correlation coefficient between two datasets, indicating the strength and direction of their relationship.