Regression totum modum

This book characterizes the field of regression analysis beyond its traditional domain of mathematics and statistics. Simply speaking, regression is a technique that relates a dependent variable to one or more independent (explanatory) variables. A regression model can show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables. Using this definition, regression methods are extended to machine learning. Consequently, the scope of this book is to present the applications of regression using the totality of methods (totum modum) one can employ in regression analysis:Linear regressionpolynomial regressiongeneral linear modelsvector generalized linear modelsbinomial regressionlogistic regressionmultinomial logistic regressionmultinomial probitordered logitmultilevel modelsfixed effectsrandom effectslinear mixed-effects modelnonlinear mixed-effects modelnonlinear regressionsupport vector regressionlasso regressionridge regressionnonparametricsemiparametricrobustquantileisotonicprincipal componentsUsing examples from the Space domain, including endoatmospheric and exoatmospheric environments, space weather, space launch, satellites, and ground sensors, many of these methods are applied. All examples are solved using the R programming language and all code and datasets are accessible from our GitHub site. Although written as a reference, the book can be adapted as an advanced textbook in regression analysis.