Multiple Linear Regression In Statsmodels Github

Leo Migdal

-Dec 4, 2025, 5:59 AM

multiple linear regression in statsmodels github

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. Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work.

Learn more Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. The code on this page uses the Statsmodels, Matplotlib, Seaborn, scikit-learn and NumPy packages. These can be installed from the terminal with the following commands: Once finished, import these packages into your Python script as follows:

This page will use the Longley Dataset from Statsmodels (see here for the documentation and the “longley” tab on this page for an example). Import it with the following: We will use the GNP and ARMED columns from the exog data frame as the exogenous variables (aka the independent variables, predictor variables or features). These contain 16 countries’ gross national product and armed forces size, respectively, from 1967. The endog variable (endogenous variable, aka the dependent variable, outcome variable or target) contains the total employment values from these countries: Divide everything by 1000 just to re-scale it all:

This tutorial comes from datarobot's blog post on multi-regression using statsmodel. I only fixed the broken links to the data. This is part of a series of blog posts showing how to do common statistical learning techniques with Python. We provide only a small amount of background on the concepts and techniques we cover, so if you’d like a more thorough explanation check out Introduction to Statistical Learning or sign up for the... Earlier we covered Ordinary Least Squares regression with a single variable. In this posting we will build upon that by extending Linear Regression to multiple input variables giving rise to Multiple Regression, the workhorse of statistical learning.

We first describe Multiple Regression in an intuitive way by moving from a straight line in a single predictor case to a 2d plane in the case of two predictors. Next we explain how to deal with categorical variables in the context of linear regression. The final section of the post investigates basic extensions. This includes interaction terms and fitting non-linear relationships using polynomial regression. In Ordinary Least Squares Regression with a single variable we described the relationship between the predictor and the response with a straight line. In the case of multiple regression we extend this idea by fitting a $p$-dimensional hyperplane to our $p$ predictors.

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/ 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 $\Sigma$ This is the third is a series of excerpts from Elements of Data Science which available from Lulu.com and online booksellers.

It’s from Chapter 10, which is about multiple regression. You can read the complete chapter here, or run the Jupyter notebook on Colab. In the previous chapter we used simple linear regression to quantify the relationship between two variables. In this chapter we’ll get farther into regression, including multiple regression and one of my all-time favorite tools, logistic regression. These tools will allow us to explore relationships among sets of variables. As an example, we will use data from the General Social Survey (GSS) to explore the relationship between education, sex, age, and income.

The GSS dataset contains hundreds of columns. We’ll work with an extract that contains just the columns we need, as we did in Chapter 8. Instructions for downloading the extract are in the notebook for this chapter. We can read the DataFrame like this and display the first few rows. We’ll start with a simple regression, estimating the parameters of real income as a function of years of education. First we’ll select the subset of the data where both variables are valid.

Multiple linear regression involves performing linear regression with more than one independent variable. As you may know, multiple regression with n predictors can be expressed as: $\beta_0$ is the intercept, representing the expected value of $y$ when all $x$-values (predictors) are 0. $\beta_1$ represents the change in $y$ for a one-unit increase in $x_{i1}$, while all other predictors are held constant. The same interpretation applies to the other predictors, $\beta_2, \beta_3, ..., \beta_n$ $\epsilon_i$ represents the residual variance that is not explained by the model.

In this lesson, you'll learn how to run your first multiple linear regression model using StatsModels. The Auto MPG dataset is a classic example of a regression dataset that was first released in 1983. MPG stands for "miles per gallon", the target to be predicted. There are also several potential independent variables. Let's look at correlations between the other variables and mpg: Since correlation is a measure related to regression modeling, we can see that there seems to be some relevant signal here, with lots of variables that have medium-to-strong correlations with MPG.

Multiple Linear Regression In Statsmodels Github

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In This Lecture, You'll Learn How To Run Your First

For Now, Let's Simplify The Model And Only Inlude 'acc',

Learn More Find Centralized, Trusted Content And Collaborate Around The

This Page Will Use The Longley Dataset From Statsmodels (see

This Tutorial Comes From Datarobot's Blog Post On Multi-regression Using