In this short course we will provide an introduction to linear regression and how to utilize it in R. We will cover the theory of linear regression as well as demonstrating how to use R to make and ...

BACKGROUND The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health, and ...

If you have experience with R or want a quick way to generate a regression with statsmodels using a pandas DataFrame, you can ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

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 ...

Let's talk about the Home Run Derby. Everyone seems to think that it really messes a player up, but I’m not so sure about that. I know a lot of people do not like math, but there is a thing called ...

This is a preview. Log in through your library . Abstract Nonlinear regression models are commonly used in toxicology and pharmacology. When fitting nonlinear models ...

The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach ...