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Regressionsanalyse verstehen - Hardcover, von Westfall Peter H. Arias - gut-
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eBay-Artikelnr.:125561171001
Artikelmerkmale
- Artikelzustand
- Book Title
- Understanding Regression Analysis
- ISBN
- 9780367458522
- Publication Year
- 2020
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Subject Area
- Mathematics, Business & Economics
- Publication Name
- Understanding Regression Analysis : a Conditional Distribution Approach
- Publisher
- CRC Press LLC
- Item Length
- 10 in
- Subject
- Probability & Statistics / General, General, Statistics
- Item Width
- 7 in
- Number of Pages
- 496 Pages
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
0367458527
ISBN-13
9780367458522
eBay Product ID (ePID)
22038264011
Product Key Features
Number of Pages
496 Pages
Language
English
Publication Name
Understanding Regression Analysis : a Conditional Distribution Approach
Publication Year
2020
Subject
Probability & Statistics / General, General, Statistics
Type
Textbook
Subject Area
Mathematics, Business & Economics
Format
Hardcover
Dimensions
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
College Audience
LCCN
2020-003321
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.536
Table Of Content
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 and Model Selection 12. Heteroscedasticity and Non-Independence 13. Models for Binary, Ordinal, and Nominal Response Variables 14. Models for Poisson and Negative Binomial Response 15. Censored Data Models 16. Outliers 17. Neural Network Regression 18. Tree Regression 19. Bookend
Synopsis
Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong . Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways. Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed "just-in-time" within chapters Simple mathematical explanations ("baby proofs") of key concepts Clear explanations and applications of statistical significance ( p -values), incorporating the American Statistical Association guidelines Use of "data-generating process" terminology rather than "population" Random - X framework is assumed throughout (the fixed- X case is presented as a special case of the random- X case) Clear explanations of probabilistic modelling, including likelihood-based methods Use of simulations throughout to explain concepts and to perform data analyses This book has a strong orientation towards science in general, as well as chapter-review and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples, it is also ideally suited to be a reference book for all scientists., This book unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks and decision trees under a common umbrella; namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong . Unlike other regression books, this one takes a realistic approach from the outset that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways. Key featuresof the book include: Numerous worked examples using the R software Key points and self-study questions displayed "just-in-time" within chapters Simple mathematical explanations ("baby proofs") of key concepts Clear explanations and applications of statistical significance ( p -values), incorporating the American Statistical Association guidelines Use of "data-generating process" terminology rather than "population" Random - X framework is assumed throughout (the fixed- X case is presented as a special case of the random- X case) Clear explanations of probabilistic modelling, including likelihood-based methods Use of simulations throughout to explain concepts and to perform data analyses This book has a strong orientation towards science in general, as well as end-of chapter and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples, it is also ideally suited to be a reference book for all scientists.
LC Classification Number
QA278.2.W475 2020
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