Objective To examine whether a multicomponent commercial fitness app with very small (‘micro’) financial incentives (FI) ...

Abstract: This paper proposes a new fuzzy regression model, i.e., the fuzzy system constructed by rule generation and iterative linear support vector regression (FS-RGLSVR) for structural risk ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and profit, and information about clients. Companies often seek out employees with ...

ABSTRACT: This research aims to develop reliable models using machine learning algorithms to precisely predict Total Dissolved Solids (TDS) in wells of the Permian basin, Winkler County, Texas. The ...

The Ramsey Regression Equation Specification Error Test (RESET) is a diagnostic test used in econometrics to detect misspecification errors in a regression model ...