What type of data does regression analysis deal with?

Regression analysis primarily deals with continuous data, which is crucial for modeling and predicting outcomes. Continuous data refers to numerical values that can take on an infinite number of values within a given range, such as height, weight, temperature, or time. The primary goal of regression analysis is to establish relationships between the dependent variable (the outcome we are trying to predict) and one or more independent variables (the predictors that influence the outcome).

By utilizing continuous data, regression can effectively compute the relationships through various mathematical expressions, such as linear or non-linear equations. This enables the generation of predictions or insights based on the identified patterns within the data.

While categorical data, discrete data, and textual data can also play roles in different types of analyses, they do not directly align with the assumptions and methodologies applied in regression analysis. Categorical data refers to variables that can be divided into groups or categories, discrete data consists of distinct or separate values, and textual data is unstructured and requires different techniques for analysis. Thus, continuous data is the most relevant type for regression analysis, aligning perfectly with its mathematical foundations and objectives.