Regression analysis is a popular and powerful statistical tool used by data scientists used to make predictions and also build regression models.

In this article, I explain what regression analysis is, list its types and highlight some of applications in real life.

Definition

Regression analysis is a set of methods used to estimate the relationship between a dependent variable and one or more independent variables. Apart from estimating relationships, regression analysis can also be used to build models that make predictions using historical values.

For example, the more money you receive, the more money you are able to spend, or the more food you eat, the more you gain weight.

Types

Regression analysis is further broken down into simple linear regression, multiple linear regression and non-linear regression.

Simple Linear Regression

Simple linear regression describes the relationship between two variables: a dependent variable and an independent variable. The simple linear equation is:

y = b0 + b1.x1 + e

y - the dependent variable
b0 - the value of y when x is zero or the y intercept
b1 - the factor that affects y as x increases or the regression coefficient
x1 - the independent variable
e - the error or difference between the observed value and the experimental value which is usually zero

When representing simple linear regression, data points are plotted and a line of best fit is drawn to represent how related the data points are.

Multiple Linear Regression

Unlike simple linear regression, multiple linear regression describes the relationship between more than one independent variable and a dependent variable. The multiple linear regression model is:

y = b0 + b1.x1 + … + bn.xn + e

y - the dependent variable
b0 - the value of y when x is zero or the y intercept
b1 - the first factor that affects y as x1 increases or the regression coefficient
x1 - the first independent variable
bn - the nth factor that affects y as x1 increases or the regression coefficient
xn - the nth independent variable
e - the error or difference between the observed value and the experimental value which is usually zero

When representing multiple linear regression on a map, the useful x values are mapped to their respective y values on the same graph ad different lines of best fit are drawn to show the relationship between each if the independent variables and the dependent variable.

Non-linear regression

Like linear regression, nonlinear regression relates two variables but in nonlinear regression, the line of best fit is curved.

Applications of Regression analysis

Forecasts and Predictions

The most common application of regression analysis is to foretell events. For example, you could predict population growth after studying population growth for the past few years. You could also predict stock prices after studying previous stock prices.

Get insights for decisions

Regression analysis can highlight patterns in data which can be used to make decisions especially in business. For example, you can use regression analysis to see accommodation demands from the beginning of the year to the end so you can know when to increase or reduce room prices.

Optimize processes

Regression analysis can help to make the best use of processes set up. For example, you can study the number of customers you get over time before and after you offer a discount to a product or you could also use regression analysis to know the product to offer discount on.

Conclusion

Notice that regression analysis predicts only one outcome and is based on cause and effect; one or more factors cause an event. Regression analysis is used to build regression models which are examples of supervised machine learning as the data set used is labelled or already has the answers, so to speak.

Did you learn anything new about regression analysis? Do you know other ways regression analysis can be applied?