Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Linear regression can be used to analyze risk. For example, a health insurance company might conduct a linear regression plotting number of claims per customer against age and discover that older ...

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software ...

Homoskedastic refers to a condition in which the variance of the error term in a regression model is constant. Learn more about its importance and how it is used.

Predicting the Future The most common use of regression in business is to predict events that have yet to occur. Demand analysis, for example, predicts how many units consumers will purchase.

To do this in R we must first make sure we limit our data frame to numerical variables (the regression function creates dummies automatically, but AirEntrain remains a categorical variable). To do ...

Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the ...

Among the most common techniques are linear regression, linear ridge regression, k-nearest neighbors regression, kernel ridge regression, Gaussian process regression, decision tree regression and ...