Often the questions we ask require us to make accurate predictions on how one factor affects an outcome. If a teacher is asked to work out how time spent writing an essay affects essay grades, it’s ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

Learn how to build a simple linear regression model in C++ using the least squares method. This step-by-step tutorial walks you through calculating the slope and intercept, predicting new values, and ...

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...

Successful investing requires the ability to distinguish long-term trends from the short-term noise that moves stock prices on a minute-to-minute basis. One way to tune out the random oscillations and ...

Regression models predict outcomes like housing prices from various inputs. Machine learning enhances regression by analyzing large, complex datasets. Different regression types address varied data ...

Polynomials are commonly used in linear regression models to capture nonlinearities in explanatory variables. It is less common, however, that polynomials are used to shift the regression coefficients ...

Regression imputation is commonly used to compensate for item nonresponse when auxiliary data are available. It is common practice to compute survey estimators by treating imputed values as observed ...

Last month we explored how to model a simple relationship between two variables, such as the dependence of weight on height 1. In the more realistic scenario of dependence on several variables, we can ...