Logistic regression is a statistical method used to model binary outcome variables, such as whether a patient recovers or not, using a set of predictors. There are many competing methods for ...

Abstract: This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation ...

A typical streamflow reconstruction model is linear: the relationship between streamflow y and the paleoclimate proxies u is modelled as Linear models do not account for the catchment state and the ...

Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression.

Learn how to build a simple linear regression model in C++ using the least squares method. This step-by-step tutorial walks you through calculating the slope and intercept, predicting new values, and ...

Abstract: This paper describes Weibull data analytics in support of grid resilience as well as interpreting test and R&D results. The target was to develop a widely accessible spreadsheet IDA ...

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...