Linear Regression In Python Using Statsmodels Data Courses

Leo Migdal

-Dec 4, 2025, 6:01 AM

linear regression in python using statsmodels data courses

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.

In the simplest terms, regression is the method of finding relationships between different phenomena. It is a statistical technique which is now widely being used in various areas of machine learning. In this article, we are going to discuss what Linear Regression in Python is and how to perform it using the Statsmodels python library. In today’s world, Regression can be applied to a number of areas, such as business, agriculture, medical sciences, and many others. Regression can be applied in agriculture to find out how rainfall affects crop yields. In medical sciences, it can be used to determine how cognitive functions change with aging.

When it comes to business, regression can be used for both forecasting and optimization. So you can use it to determine the factors that influence, say productivity of employees and then use this as a template to predict how changes in these factors are going to bring changes... This can help you focus on factors that matter the most so that you can optimize them and bring about an increase in the overall productivity of employees. When performing regression analysis, you are essentially trying to determine the impact of an independent variable on a dependent variable. Let’s take our productivity problem as an example. We know that productivity of an employee is dependent on other factors.

So productivity is the dependent variable. It may be dependent on factors such as age, work-life balance, hours worked, etc. These are the independent variables. Included with.css-t3io8q{-webkit-align-items:baseline;-webkit-box-align:baseline;-ms-flex-align:baseline;align-items:baseline;background-color:rgba(255, 255, 255, 0.01);border-radius:4px;-webkit-box-decoration-break:clone;box-decoration-break:clone;color:var(--wf-text--link, #0065D1);display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;font-family:Studio-Feixen-Sans,Arial,sans-serif;font-size:inherit;font-weight:800;line-height:inherit;outline:0;-webkit-text-decoration:underline;text-decoration:underline;text-decoration-color:transparent;text-decoration-thickness:1.25px;-webkit-transition:box-shadow 125ms ease-out,background-color 125ms ease-out,text-decoration-color 125ms ease-out;transition:box-shadow 125ms ease-out,background-color 125ms ease-out,text-decoration-color 125ms ease-out;}.css-t3io8q:hover{background-color:var(--wf-bg--hover, rgba(48, 57, 105, 0.06));}.css-t3io8q:hover{box-shadow:0 0 0 2px var(--wf-bg--hover, rgba(48, 57, 105, 0.06));text-decoration-color:var(--wf-text--link, #0065D1);}Premium or Teams I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understanding what’s happening under the hood. That’s where statsmodels shines.

Unlike scikit-learn, which optimizes for prediction, statsmodels gives you the statistical framework to understand relationships in your data. Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter. Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS). Think of it as the statistical counterpart to scikit-learn. Where scikit-learn focuses on prediction accuracy, statsmodels focuses on inference: understanding which variables matter, quantifying uncertainty, and validating assumptions. The library gives you detailed statistical output including p-values, confidence intervals, and diagnostic tests.

This matters when you’re not just predicting house prices but explaining to stakeholders why square footage matters more than the number of bathrooms. Start with the simplest case: one predictor variable. Here’s a complete example using car data to predict fuel efficiency: Python is popular for statistical analysis because of the large number of libraries. One of the most common statistical calculations is linear regression. statsmodels offers some powerful tools for regression and analysis of variance.

Here's how to get started with linear models. statsmodels is a Python library for running common statistical tests. It's especially geared for regression analysis, particularly the kind you'd find in econometrics, but you don't have to be an economist to use it. It does have a learning curve. but once you get the hang of it, you'll find that it's a lot more flexible to use than the regression functions you'll find in a spreadsheet program like Excel. It won't make the plot for you, though.

If you want to generate the classic scatterplot with a regression line drawn over it, you'll want to use a library like Seaborn. One advantage of using statsmodels is that it's cross-checked with other statistical software packages like R, Stata, and SAS for accuracy, so this might be the package for you if you're in professional or... If you just want to determine the relation ship of a dependent variable (y), or the endogenous variable in econometric and statsmodels parlance, vs the exogenous, independent, or "x" variable, you can do this... 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). Learn what regression analysis is and how to implement it in Python in this definitive step-by-step mini-course. If you’re interested, you can follow along with the video and the Linear Regression in Python Juypter Notebook. Regression analysis is a common statistical method used in finance to determine the relationship between variables.

The process helps understand the factors that are important and irrelevant and how they affect each other. For instance, if we want to predict the price of homes, the home price prediction would be the dependent variable, and the independent variable or independent variables would be the independent variables. Examples of independent variables or factors influencing the home price might be square feet, the number of rooms, garage, finished basement, etc. Any data scientist must comprehend the fundamentals of linear regression because it is a key algorithm in machine learning and statistics. Numerous libraries in Python make it easier to implement this approach, with Statsmodels being one of the most potent. This article explores the use of linear regression using Statsmodels, using examples drawn from actual data to aid comprehension.

By fitting a linear equation to the observed data, linear regression is a statistical technique that models the relationship between two variables. While one variable is the dependent variable whose change is being examined, the other is the explanatory (independent) variable. A Python package created specifically for statistics is called Statsmodels. It is built on top of other strong libraries like Matplotlib, SciPy, and NumPy. A full range of statistical tests is available through Statsmodels, which also offers robust estimates in several statistical models. Make sure you have installed Statsmodels and any other required libraries before you begin ?

Let's begin with a straightforward illustration of linear regression in which there is just one independent variable. We'll use the mtcars dataset, which is a built-in dataset in Statsmodels, for this example. This information includes eleven characteristics of automobile performance and design for 32 different vehicles, together with fuel consumption data (mpg). 4.4 rating at DataCamp based on 40 ratings Start your review of Introduction to Regression with statsmodels in Python

Linear Regression In Python Using Statsmodels Data Courses

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