Linear Regression Statsmodels Github Pages

Leo Migdal

-Dec 4, 2025, 5:22 AM

linear regression statsmodels github pages

Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See Module Reference for commands and arguments. $Y = X\beta + \epsilon$, where $\epsilon\sim N\left(0,\Sigma\right).$ Depending on the properties of $\Sigma$, we have currently four classes available: GLS : generalized least squares for arbitrary covariance $\Sigma$

statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at Recent improvements are highlighted in the release notes https://www.statsmodels.org/stable/release/ Simple linear regression is a basic statistical method to understand the relationship between two variables.

One variable is dependent, and the other is independent. Python’s statsmodels library makes linear regression easy to apply and understand. This article will show you how to perform simple linear regression using statsmodels. Simple Linear Regression is a statistical method that models the relationship between two variables. The general equation for a simple linear regression is: This equation represents a straight-line relationship.

Changes in X lead to proportional changes in Y. Simple linear regression helps to understand and measure this relationship. It is a fundamental technique in statistical modeling and machine learning. First, install statsmodels if you haven’t already: We will use a simple dataset where we analyze the relationship between advertising spending (X) and sales revenue (Y). This module implements standard regression models:

Generalized Least Squares (GLS) Ordinary Least Squares (OLS) Weighted Least Squares (WLS) Generalized Least Squares with autoregressive error terms GLSAR(p) Models are specified with an endogenous response variable and an exogenous design matrix and are fit using their fit method. Subclasses that have more complicated covariance matrices should write over the ‘whiten’ method as the fit method prewhitens the response by calling ‘whiten’. General reference for regression models: In this article, we will discuss how to use statsmodels using Linear Regression in Python. Linear regression analysis is a statistical technique for predicting the value of one variable(dependent variable) based on the value of another(independent variable).

The dependent variable is the variable that we want to predict or forecast. In simple linear regression, there's one independent variable used to predict a single dependent variable. In the case of multilinear regression, there's more than one independent variable. The independent variable is the one you're using to forecast the value of the other variable. The statsmodels.regression.linear_model.OLS method is used to perform linear regression. Linear equations are of the form:

Syntax: statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) Return: Ordinary least squares are returned. Importing the required packages is the first step of modeling. The pandas, NumPy, and stats model packages are imported. So far, you learned how linear regression and R-Squared (coefficient of determination) work "under the hood" and created your own versions using NumPy. Going forward, you're going to use a Python library called StatsModels to do the modeling and evaluation work for you!

StatsModels is a powerful Python package for many types of statistical analyses. In particular, as you may have guessed from the name, StatsModels emphasizes statistical modeling, particular linear models and time series analysis. You can check out the User Guide for an overview of all of the available models. When using StatsModels, we'll need to introduce one more set of terminology: endogenous and exogenous variables. You'll see these as argument names endog and exog in the documentation for the models, including OLS (ordinary least squares linear regression). These are simply the names used by StatsModels for the independent and dependent variables.

This table is drawn from the StatsModels documentation: I created this notebook to demonstrate multiple linear regression using statsmodels. I use synthetic data containing money spent on TV, radio and newspaper advertisements and the corresponding sale values. All units are in thousands of dollars. Multiple Linear Regression is a technique that estimates the relationship between 1 continuous dependent variable y and 2 or more independent variables X1,...,Xn. $\text{y} = \beta_{0} + \beta_{1}*X_{1} + ...

+ \beta_{n}*X_{n}$ Some statements must hold true to justify the use of linear regression: In this lecture, you'll learn how to run your first multiple linear regression model. This lesson will be more of a code-along, where you'll walk through a multiple linear regression model using both statsmodels and scikit-learn. Recall the initial regression model presented. It determines a line of best fit by minimizing the sum of squares of the errors between the models predictions and the actual data.

In algebra and statistics classes, this is often limited to the simple 2 variable case of $y=mx+b$, but this process can be generalized to use multiple predictive variables. The code below reiterates the steps you've seen before: For now, let's simplify the model and only inlude 'acc', 'horse' and the three 'orig' categories in our final data. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page

SARIMAX: Frequently Asked Questions (FAQ) State space modeling: Local Linear Trends Fixed / constrained parameters in state space models This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page

Linear Regression Statsmodels Github Pages

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