Knihobot

Regression

Models, Methods and Applications

Hodnocení knihy

Parametry

  • 712 stránek
  • 25 hodin čtení

Více o knize

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Vydání

Nákup knihy

Regression, Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian D Marx

Jazyk
Rok vydání
2013
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Doručení

Platební metody

4,2
Velmi dobrá
7 Hodnocení

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Titul
Regression
Podtitul
Models, Methods and Applications
Jazyk
anglicky
Vydavatel
Springer
Rok vydání
2013
Vazba
pevná
Počet stran
712
ISBN10
3642343325
ISBN13
9783642343322
Série
Hodnocení
4,15 z 5
Anotace
The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.