Regression is a statistical technique used to establish a relationship between a dependent variable and one or more independent variables. The goal of regression analysis is to model and understand the relationship between the variables, and to use this model to make predictions about the dependent variable based on the values of the independent variables.

The goal of regression analysis is to build a model that can predict the value of the dependent variable based on the values of the independent variables. The most common type of regression analysis is linear regression, which models the relationship between the dependent variable and one or more independent variables as a straight line. The line is fitted to the data using a technique called least squares, which minimizes the sum of the squared differences between the observed values of the dependent variable and the predicted values of the dependent variable.

Figure 1. classification vs regression. [1]

Figure 1 shows classification vs regression. There are several types of regression analysis, including linear regression, logistic regression, and polynomial regression. Linear regression is the most commonly used type of regression, and it models a linear relationship between the dependent variable and one or more independent variables. Logistic regression, on the other hand, models the probability of a binary outcome, such as whether a customer will make a purchase or not.

Linear regression can be used to answer questions such as:

• What is the relationship between a person's age and their income?
• How does the price of a product affect its sales volume?
• How does the level of education impact a person's job performance?

Regression analysis can be used for both descriptive and predictive purposes. Descriptive regression analysis is used to describe the relationship between the variables and to identify the factors that influence the dependent variable. Predictive regression analysis, on the other hand, is used to predict the value of the dependent variable for new observations based on the values of the independent variables.

Regression analysis is widely used in various fields, including economics, finance, psychology, and biology. It can be used for predicting sales, analyzing the impact of marketing campaigns, determining the relationship between education and income, and many other applications.

References:

1. https://www.javatpoint.com/regression-vs-classification-in-machine-learning

Cite this article:

Hana M (2023), Regression, AnaTechmaz, pp.214