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 regression
polynomial regression
general linear models
vector generalized linear models
binomial regression
logistic regression
multinomial logistic regression
multinomial probit
ordered logit
multilevel models
fixed effects
random effects
linear mixed-effects model
nonlinear mixed-effects model
nonlinear regression
support vector regression
lasso regression
ridge regression
nonparametric
semiparametric
robust
quantile
isotonic
principal components

Using 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.