In epidemiological studies using linear regression, it is often necessary for reasons of economy or unavailability of data to use as the independent variable not the variable ideally demanded by the ...

insights from industryDr. David Honigs, Ph.D.Field Applications ScientistPerkinElmer, Inc. In this interview, Dr. David Honigs, Ph.D, the Field Applications Scientist ...

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 ...

Not a big fan of Microsoft Excel. Oh sure, it does the job, and it does it quite well (usually). I really just hate starting up that program. Let me say that historically, I think Excel has had a HUGE ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

In this article, a Bayesian model for a constrained linear regression problem is studied. The constraints arise naturally in the context of predicting the new crop of apples for the year ahead. We ...