A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern ...

The idea of an always-on journey captures this reality. Decisions are shaped gradually, through signals, prompts, and small ...

A variety of linear models are available to represent common active electronic devices such as transistors and vacuum tubes. Devices operating under large-signal conditions often require nonlinear ...

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 ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

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 ...

In the early 1970s, statisticians had difficulty in analysing data where the random variation of the errors did not come from the bell-shaped normal distribution. Besides normality, these traditional ...

Troy Segal is an editor and writer. She has 20+ years of experience covering personal finance, wealth management, and business news. Eric's career includes extensive work in both public and corporate ...