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linear regression
This term is a technical expression used primarily in statistics, data science, and econometrics. It describes a specific mathematical approach to predictive modeling where the goal is to find the best-fitting straight line through a set of data points. While "regression" can refer to various types of statistical analysis, the modifier "linear" specifies that the relationship between variables is assumed to be a straight line.
In professional and academic writing, it is treated as a singular conceptual entity. It is rarely used in casual conversation unless the speaker is discussing quantitative research or machine learning. Users should distinguish it from "logistic regression," which is used for binary outcomes rather than continuous numerical values.
Meanings
Examples
We can use linear regression to predict future sales based on advertising spend.
I wonder if linear regression is the best model for this dataset or if I should try something nonlinear.
I wonder if linear regression is the best model for this dataset or if I should try something nonlinear.
The professor explained how linear regression finds the line of best fit.
Let's apply linear regression to see if there is a correlation between study hours and test scores.
The analyst performed a simple linear regression to identify the primary trend.
Does this data actually satisfy the assumptions required for linear regression?
Linear regression is often the first algorithm taught in an introductory data science course.
He realized that the relationship was too complex for a basic linear regression.
He realized that the relationship was too complex for a basic linear regression.
The research paper utilizes multiple linear regression to account for several independent variables.
The research paper utilizes multiple linear regression to account for several independent variables.