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Lulu Regression totum modum

Name: Regression totum modum
Brand: Lulu

Algorithms

Gone for 34 days. Could come back at any time — we're watching for you.

Out of stock 34 days · last price £73 · longest previous gap was 1 days

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Description

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.

Product Specifications

Brand: Lulu
Format: hardcover
ASIN: 1329337328
Category: Books > Subjects > Computing & Internet > Programming > Algorithms
Domain: Amazon UK
Release Date: 20 February 2023
Listed Since: 02 May 2023

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Lulu Regression totum modum

Gone for 34 days. Could come back at any time — we're watching for you.

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