• Produktbild: Understanding Regression Analysis
  • Produktbild: Understanding Regression Analysis

Understanding Regression Analysis A Conditional Distribution Approach

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.05.2022

Abbildungen

173 SW-Abb., 39 Tabellen

Verlag

Taylor & Francis

Seitenzahl

516

Maße (L/B/H)

25,4/17,8/2,8 cm

Gewicht

960 g

Sprache

Englisch

ISBN

978-0-367-49351-6

Beschreibung

Rezension

"...The authors suggest their book is suitable for those who are "research-oriented", regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book's chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."
- Ryan Andrews, ISCB News, June 2021

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.05.2022

Abbildungen

173 SW-Abb., 39 Tabellen

Verlag

Taylor & Francis

Seitenzahl

516

Maße (L/B/H)

25,4/17,8/2,8 cm

Gewicht

960 g

Sprache

Englisch

ISBN

978-0-367-49351-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Understanding Regression Analysis
  • Produktbild: Understanding Regression Analysis
  • 1. Introduction to Regression Models
    2. Estimating Regression Model Parameters
    3. The Classical Model and Its Consequences
    4. Evaluating Assumptions
    5. Transformations
    6. The Multiple Regression Model
    7. Multiple Regression from the Matrix Point of View
    8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity
    9. Polynomial Models and Interaction (Moderator) Analysis
    10. ANOVA, ANCOVA, and Other Applications of Indicator Variables
    11. Variable Selection
    12. Heteroscedasticity and Non-independence
    13. Models for Binary, Nominal, and Ordinal Response Variables
    14. Models for Poisson and Negative Binomial Response
    15. Censored Data Models
    16. Outliers, Identification, Problems, and Remedies (Good and Bad)
    17. Neural Network Regression
    18. Regression Trees
    19. Bookend