machine_learning.ordinary_least_squares_regression
==================================================

.. py:module:: machine_learning.ordinary_least_squares_regression

.. autoapi-nested-parse::

   Ordinary Least Squares Regression (OLSR):

   Ordinary Least Squares Regression (OLSR) is a statistical method for
   estimating the parameters of a linear regression model.
   It is the most commonly used regression method,
   and it is based on the principle of minimizing
   the sum of the squared residuals.

   Below is a simple implementation of OLSR
   without using any external libraries.

   WIKI: https://en.wikipedia.org/wiki/Ordinary_least_squares



Attributes
----------

.. autoapisummary::

   machine_learning.ordinary_least_squares_regression.x_points


Functions
---------

.. autoapisummary::

   machine_learning.ordinary_least_squares_regression.ols_regression


Module Contents
---------------

.. py:function:: ols_regression(x_point: numpy.ndarray, y_point: numpy.ndarray) -> tuple

   Performs Ordinary Least Squares Regression (OLSR) on the given data.

   Args:
       x: The independent variable.
       y: The dependent variable.

   Returns:
       a (float): The intercept of the regression line.
       b (float): The slope of the regression line.

   Examples:
   >>> x = np.array([1, 2, 3, 4, 5])
   >>> y = np.array([2, 4, 6, 8, 10])
   >>> a, b = ols_regression(x, y)
   >>> float(a)  # Intercept should be 0.0
   0.0
   >>> float(round(b, 2))  # Slope should be 2.0
   2.0


.. py:data:: x_points