8 Simple Linear Regression Basic Analytics In Python

Simple linear regression models the relationship between a dependent variable and a single independent variable. In this article, we will explore simple linear regression and it's implementation in Python using libraries such as NumPy, Pandas, and scikit-learn. Simple Linear Regression aims to describe how one variable i.e the dependent variable changes in relation with reference to the independent variable. For example consider a scenario where a company wants to predict sales based on advertising expenditure. By using simple linear regression the company can determine if an increase in advertising leads to higher sales or not. The below graph explains the relationship between advertising expenditure and sales using simple linear regression:

The relationship between the dependent and independent variables is represented by the simple linear equation: In this equation m signifies the slope of the line indicating how much y changes for a one-unit increase in x, a positive m suggests a direct relationship while a negative m indicates an... There are many ways to do linear regression in Python. We have already used the heavyweight Statsmodels library, so we will continue to use it here. It has much more functionality than we need, but it provides nicely-formatted output similar to SAS Enterprise Guide. The method we will use to create linear regression models in the Statsmodels library is OLS().

OLS stands for “ordinary least squares”, which means the algorithm finds the best fit line my minimizing the squared residuals (this is “least squares”). The “ordinary” part of the name gives us the sense that the type of linear regression we are seeing here is just the tip of the methodological iceberg. There is a whole world of non-ordinary regression techniques out there intended to address this or that methodological problem or circumstance. But since this is a basic course, we will stick with ordinary least squares. Recall the general format of the linear regression equation: \(Y = \beta_0 + \beta_1 X_1 + ... + \beta_n X_n\), where \(Y\) is the value of the response variable and \(X_i\) is the value of the explanatory variable(s).

If we think about this equation in matrix terms, we see that Y is a 1-dimensional matrix: it is just a single column (or array or vector) of numbers. In our case, this vector corresponds to the compressive strength of different batches of concrete measured in megapascals. The right-hand side of the equation is actually a 2-dimensional matrix: there is one column for our X variable and another column for the constant. We don’t often think about the constant as a column of data, but the Statsmodels library does, which is why we are talking about it. Creating a linear regression model in Statsmodels thus requires the following steps: Recommended Video CourseStarting With Linear Regression in Python

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Starting With Linear Regression in Python Linear regression is a foundational statistical tool for modeling the relationship between a dependent variable and one or more independent variables. It’s widely used in data science and machine learning to predict outcomes and understand relationships between variables. In Python, implementing linear regression can be straightforward with the help of third-party libraries such as scikit-learn and statsmodels. By the end of this tutorial, you’ll understand that:

To implement linear regression in Python, you typically follow a five-step process: import necessary packages, provide and transform data, create and fit a regression model, evaluate the results, and make predictions. This approach allows you to perform both simple and multiple linear regressions, as well as polynomial regression, using Python’s robust ecosystem of scientific libraries. A complete hands-on guide to simple linear regression, including formulas, intuitive explanations, worked examples, and Python code. Learn how to fit, interpret, and evaluate a simple linear regression model from scratch. This article is part of the free-to-read Data Science Handbook Choose your expertise level to adjust how many terms are explained.

Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions. Simple linear regression is the foundation of predictive modeling in data science and machine learning. It's a statistical method that models the relationship between a single independent variable (feature) and a dependent variable (target) by fitting a straight line to observed data points. Think of it as finding a straight line that passes through or near your data points on a scatter plot. Simple linear regression offers simplicity and interpretability.

When you have two variables that seem to have a linear relationship, this method helps you understand how one variable changes with respect to the other. For example, you might want to predict house prices based on square footage, or understand how study hours relate to test scores. Simple linear regression is a technique that we can use to understand the relationship between a single explanatory variable and a single response variable. This technique finds a line that best “fits” the data and takes on the following form: This equation can help us understand the relationship between the explanatory and response variable, and (assuming it’s statistically significant) it can be used to predict the value of a response variable given the value... This tutorial provides a step-by-step explanation of how to perform simple linear regression in Python.

For this example, we’ll create a fake dataset that contains the following two variables for 15 students: In this post, we will be putting into practice what we learned in the introductory linear regression article. Using Python, we will construct a basic regression model to make predictions on house prices. Linear regression is a type of analysis used to make predictions based on known information and a single independent variable. In the previous post, we discussed predicting house prices (dependent variable) given a single independent variable, its square footage (sqft). We are going to see how to build a regression model using Python, pandas, and scikit-learn.

Let's kick this off by setting up our environment. Let's start by installing Python from the official repository: https://www.python.org/downloads/ Download the package that is compatible with your operating system, and proceed with the installation. To make sure Python is correctly installed on your machine, type the following command into your terminal: In this tutorial, we will discuss how to perform a linear regression analysis using Python. Specifically, we will use the well-known package NumPy.

This package allows you to work with multidimensional data arrays and perform particular calculations with them. You can find more information about this package in its official guide. Additionally, we will use the scikit-learn library to perform the actual analysis. Please make sure you have both installed! We will not go into detail regarding the theory of regression analysis and the interpretation of outcomes. Rather, we will focus on how to produce results using Python.

NOTE: How to install Numpy and scikit-learn? Are you not sure how to install Numpy? Please check out the tutorial on Modules and Packages. If you are working with an Anaconda distribution of Python, NumPy should already be installed. The sections below will guide you through the process of performing a simple linear regression using scikit-learn and NumPy. That is, we will only consider one regressor variable (x).

The next chapter will discuss Multiple Linear Regression (MLR) with multiple regressor variables. First of all, we should start by importing NumPy and the classes that we need from scikit-learn at the start of our script. Linear Regression using Salary and Years of Experience DataData Source: Salary_dataset.csv KaggleThe salary data set includes 2 columns: Years Experience which will be our independent variable (X) and Salary (Y).Linear regression is a fundamental... The primary goal of linear regression is to predict the value of the dependent variable based on the values of the independent variables (Chat GPT) For this example: First, we want to see if there is a correlation between the 2 variables by building a regression line and calculating r squared. Then we want to assess the significance of the relationship using the p-value to test the null hypothesis that there is no relationship between X and Y (X does not predict Y).Simple Linear Regression...

This repository contains a comprehensive tutorial on Simple Linear Regression, one of the most fundamental algorithms in machine learning and statistics. Through this hands-on implementation, we explore how to build, evaluate, and validate a linear regression model that predicts a person's height based on their weight using Python and scikit-learn. Simple Linear Regression is often the first machine learning algorithm that students encounter, and for good reason - it provides an intuitive introduction to core concepts like model training, evaluation metrics, and assumption testing... The primary objectives of this tutorial are to: This tutorial provides in-depth coverage of the following machine learning concepts: Ensure you have Python 3.7+ installed along with the following packages:

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