Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the ...

Data analysis is a fundamental process in any project. However, data can be lumped into different types, with categorical and continuous data seeming almost opposed at first glance. That said ...

Various approaches have been developed to deal with missing covariate problems in regression analysis when the data are missing at random. Among them, three main non-likelihood approaches are through ...

Classification and regression trees are ideally suited for the analysis of complex ecological data. For such data, we require flexible and robust analytical methods, which can deal with nonlinear ...

Be sensitive to ethical issues that are involved in dealing with data science applications arising in real world situations. Clearly communicate the results of a data science analysis to a ...

This family includes models for categorical data such as logistic, probit, and complementary log-log regression for binomial data and Poisson regression for count data, as well as continuous models ...