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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)…mehr

Produktbeschreibung
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.
Autorenporträt
LinkedIn: https://www.linkedin.com/in/jeffreystrickland/ Related Books by the Author: Data Science Applications using R. Copyright © 2017. Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-81042-0, Predictive Crime Analysis using R. Copyright© 2018, Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-43159-5, Logistic Regression - Inside-Out. Copyright © 2017 by Jeffrey S. Strickland. Glasstree, Inc. ISBN 978-1-365-27041-3, Time Series Analysis using Open-Source Tools. Copyright© 2016, Jeffrey Strickland Glasstree, Inc. ISBN 978-1-5342-0100-2 , Predictive Analytics using R. Copyright © 2016 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-84101-7, Data Analytics Using Open-Source Tools. Copyright © 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-365-21384-7, Data Science and Analytics for Ordinary People. Copyright © 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-28062-5, Operations Research using Open-Source Tools. Copyright © 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-329-00404-7, Missile Flight Simulation - Surface-to-Air Missiles, 2nd Edition. Copyright © 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-64495-3, Verification and Validation for Modeling and Simulation Copyright © 2014 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-74061-7, Mathematical Modeling of Warfare and Combat Phenomenon. Copyright © 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-45839255-8 , Simulation Conceptual Modeling. Copyright © 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-105-18162-7, Discrete Event Simulation using ExtendSim 8. Copyright © 2010 by Jeffrey S. Strickland. Lulu.com. ISBN 978-0-557-72821-3