Understanding Linear Regression: A Beginner’s Guide

Linear regression is a popular and widely used statistical method for modelling the relationship between two variables. It is a simple and powerful tool that can help us make predictions based on observed data. In this article, we will explain linear regression in a way that is easy to understand, even for those with no prior knowledge of statistics or mathematics.

The linear regression equation can be written as:

y = b0 + b1 * x

Where: y is the dependent variable, also called the output or response x is the independent variable, also called the predictor or input b0 is the y-intercept, the point where the line crosses the y-axis b1 is the slope, the change in y for each unit change in x

Think of y as the height of a person, and x as their age. By looking at data points of different people and their heights and ages, we can try to fit a line that best describes the relationship between the two variables. This line can be used to predict a person’s height given their age, or estimate a person’s age given their height.

The line is drawn such that the sum of the squared distances between the observed data points and the line is minimized. This is known as the least squares method and it helps us find the values of b0 and b1 that best fit the data.

Imagine that you work at a pet store and you have been keeping track of the weight of a group of dogs over time. You have gathered the following data: