Machine learning and statistics have many applications in business and the social sciences. However, the theory is often intimidating and not easily understood. In this series of articles, I aim to demystify the concepts behind the common tools used in data science and machine learning, starting with linear regression. Linear regression is a statistical method that allows us to describe relationships between variables (distinct things that can be measured or recorded, such as height, weight, and hair colour). It is an extension of the General Linear Model, a framework to describe how a variable of interest can be modelled using other predictor variables. In simple linear regression (SLR), we focus on the relationship between two continuous variables, x and y (hence, simple).

When the UK government decided to cancel school exams due to the coronavirus pandemic, they gave examination regulators Ofqual a challenge: allocate grades to students anyway, and make sure the grades given out this year are equivalent in standard to previous years. Ofqual's solution was to create an algorithm – a computer program designed to predict what grades the students would have received if they had taken exams. Unfortunately, when the computer-generated grades were issued, 40 per cent of A-Level students got lower grades than their teachers had predicted. Promised university places were withdrawn. Lawyers offered to take legal action against Ofqual.