Here, we look at how to use data imported into Microsoft Excel to perform a linear regression and how to interpret the results. Linear regression models the relationship between a dependent and ...

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population ...

Linear regression may be the most basic and accessible machine learning (ML) algorithm, but it’s also one of the fastest and most powerful. As a result, professionals in business, science, and ...

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, ...

Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the linear support vector regression (linear SVR) technique, where the goal is to predict a single numeric ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

It can be highly beneficial for companies to develop a forecast of the future values of some important metrics, such as demand for its product or variables that describe the economic climate. There ...