AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...

David Gerbing from the School of Business at Portland State University introduces lessR, a tool designed to facilitate professional-quality data visualizations and data analysis without programming re ...

The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

In this short course we will cover how to analyze simple and multiple linear regression models. You will learn concepts in linear regression such as: 1) How to use the F-test to determine if your ...

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 ...

The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

For an academic researcher who first trained as a philosopher, then as a psychologist, Robyn Dawes was a practical fellow. He would tell a story from his time working in a psychiatric ward in the ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...