# Understanding Regression Analysis: A Conditional Distribution Approach

PDF, ePUB

- eBook:Understanding Regression Analysis: A Conditional Distribution Approach
- Author:Peter H. Westfall, Andrea L. Arias
- Edition:1 edition
- Categories:
- Data:July 15, 2020
- ISBN:0367458527
- ISBN-13:9780367458522
- Language:English
- Pages:514 pages
- Format:PDF, ePUB

**Book Description**

*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

**Content**

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

## Download **Understanding Regression Analysis: A Conditional Distribution Approach** PDF or ePUB format free

**Understanding Regression Analysis: A Conditional Distribution Approach**

Free sample