Automated multiple regression model-building techniques often hide important aspects of data from the data analyst. Such features as nonlinearity, collinearity, outliers, and points with high leverage ...

Lesson 10 Multiple Linear Regression 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 ...

Multiple Regression You can create multiple regression models quickly using the fit variables dialog. You can use diagnostic plots to assess the validity of the models and identify potential outliers ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Multiple regression models with survey data Regression becomes a more useful tool when researchers want to look at multiple factors simultaneously. If we want to know whether the racial divide ...

In this study we present results of a dynamical-statistical modeling approach for phenology in southeastern Germany, combining climate change simulations provided by a high resolution, ...

Within a multiple regression model, none or few of the covariates are statistically significant, yet the overall variance of the dependent variable explained by the covariates is high (this is ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

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 ...