Introduction
Artificial Intelligence (AI) has paved the way for numerous advancements in various fields. One of the core concepts within AI is machine learning, which comprises supervised learning, unsupervised learning, and reinforcement learning. Each learning type has its own set of algorithms.
Among these algorithms, linear regression, a supervised learning technique, is a fundamental concept in machine learning and artificial intelligence. It is employed to model the linear relationship between two variables, where one is considered the dependent variable and the other is the independent variable. Univariate linear regression, specifically, focuses on predicting the dependent variable using a single independent variable, thus simplifying the modeling process. In this article, we will discuss the concept of univariate linear regression, which is used to predict a dependent variable based on a single independent variable. We will also explore how univariate linear regression is used in AI.
What is Linear Regression?
Linear regression is a statistical technique that helps us uncover the relationships between variables. It enables us to create a model that explains how one variable, known as the dependent or response variable, changes in response to the variations in another variable, referred to as the independent or predictor variable. By understanding these relationships, we can make predictions and better comprehend the dynamics at play within a particular dataset.
The main purpose of using a linear model is to assess connections, enabling us to explain changes in an outcome variable by considering a chosen model and an associated prediction error:
Outcome = Model + Error