IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

Linear regression specifically aims to solve regression problems, where the objective is to predict a continuous numerical value based on the relationship between one or more predictor variables and a response variable. In regression analysis, the focus is on modeling the relationship so that we can make predictions or understand the impact of changes in the predictors on the response outcome.

In the context of linear regression, the relationship is modeled using a linear equation, which describes how the predicted value of the response variable changes with the predictor variables. This capability makes linear regression particularly useful in various fields for making quantitative forecasts, such as predicting sales amounts based on advertising spend or estimating housing prices based on various attributes of the property.

The other types of problems mentioned—classification, clustering, and association—serve different purposes. Classification is used for predicting categorical outcomes, clustering involves grouping similar data points without predefined labels, and association aims to discover rules that describe large portions of data, typically used in market basket analysis. Thus, linear regression is distinct in its application to predicting continuous outcomes, firmly categorizing it as a regression problem.