Linear Regression Models: Applications in R: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Autor John P. Hoffmannen Limba Engleză Paperback – 13 sep 2021
After furnishing some background material, the author explains how to estimate simple and multiple LRMs in R, including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model, adjusting for measurement error, understanding the effects of influential observations, and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions.
Features
- Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied.
- Uses numerous graphs in R to illustrate the model’s results, assumptions, and other features.
- Does not assume a background in calculus or linear algebra, rather, an introductory statistics course and familiarity with elementary algebra are sufficient.
- Provides many examples using real-world datasets relevant to various academic disciplines.
- Fully integrates the R software environment in its numerous examples.
John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 535.06 lei 6-8 săpt. | |
CRC Press – 13 sep 2021 | 535.06 lei 6-8 săpt. | |
Hardback (1) | 1264.21 lei 6-8 săpt. | |
CRC Press – 13 sep 2021 | 1264.21 lei 6-8 săpt. |
Din seria Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- 9% Preț: 612.60 lei
- Preț: 356.63 lei
- 8% Preț: 548.26 lei
- 8% Preț: 406.17 lei
- Preț: 349.02 lei
- 8% Preț: 392.16 lei
- Preț: 373.57 lei
- 9% Preț: 1277.83 lei
- Preț: 349.09 lei
- 9% Preț: 642.04 lei
- Preț: 360.75 lei
- Preț: 371.62 lei
- 8% Preț: 404.91 lei
- 9% Preț: 641.03 lei
- 8% Preț: 389.70 lei
- Preț: 454.19 lei
- 8% Preț: 420.83 lei
- Preț: 407.85 lei
- 13% Preț: 311.46 lei
- 15% Preț: 425.18 lei
- 22% Preț: 333.10 lei
- 18% Preț: 1090.54 lei
- 18% Preț: 781.29 lei
- 15% Preț: 608.18 lei
- 13% Preț: 312.44 lei
- 15% Preț: 493.07 lei
- 13% Preț: 311.46 lei
- 18% Preț: 769.09 lei
- Preț: 445.49 lei
- 18% Preț: 1204.52 lei
- 13% Preț: 311.46 lei
- 25% Preț: 529.73 lei
- Preț: 433.24 lei
- 25% Preț: 882.23 lei
Preț: 535.06 lei
Preț vechi: 629.48 lei
-15% Nou
Puncte Express: 803
Preț estimativ în valută:
102.41€ • 107.44$ • 84.95£
102.41€ • 107.44$ • 84.95£
Carte tipărită la comandă
Livrare economică 29 ianuarie-12 februarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780367753665
ISBN-10: 0367753669
Pagini: 436
Ilustrații: 13 Tables, black and white; 72 Line drawings, black and white; 72 Illustrations, black and white
Dimensiuni: 156 x 234 x 23 mm
Greutate: 0.62 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
ISBN-10: 0367753669
Pagini: 436
Ilustrații: 13 Tables, black and white; 72 Line drawings, black and white; 72 Illustrations, black and white
Dimensiuni: 156 x 234 x 23 mm
Greutate: 0.62 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Cuprins
1. Introduction
2. Review of Elementary Statistical Concepts
3. Simple Linear Regression Models
4. Multiple Linear Regression Models
5. The ANOVA Table and Goodness-of-Fit Statistics
6. Comparing Linear Regression Models
7. Indicator Variables in Linear Regression Models
8. Independence
9. Homoscedasticity
10. Collinearity and Multicollinearity
11. Normality, Linearity, and Interaction Effects
12. Model Specification
13. Measurement Errors
14. Influential Observations: Leverage Points and Outliers
15. Multilevel Linear Regression Models
16. A Brief Introduction to Logistic Regression
17. Conclusions
Appendix A: Data Management
Appendix B: Using Simulations to Examine Assumptions of Linear Regression Models
Appendix C: Formulas
Appendix C: User-Written R Packages Employed in Examples
2. Review of Elementary Statistical Concepts
3. Simple Linear Regression Models
4. Multiple Linear Regression Models
5. The ANOVA Table and Goodness-of-Fit Statistics
6. Comparing Linear Regression Models
7. Indicator Variables in Linear Regression Models
8. Independence
9. Homoscedasticity
10. Collinearity and Multicollinearity
11. Normality, Linearity, and Interaction Effects
12. Model Specification
13. Measurement Errors
14. Influential Observations: Leverage Points and Outliers
15. Multilevel Linear Regression Models
16. A Brief Introduction to Logistic Regression
17. Conclusions
Appendix A: Data Management
Appendix B: Using Simulations to Examine Assumptions of Linear Regression Models
Appendix C: Formulas
Appendix C: User-Written R Packages Employed in Examples
Notă biografică
John P. Hoffmann is a professor of sociology at Brigham Young University. He holds a PhD in Criminology from the State University of New York at Albany and a Masters of Public Health (MPH) from Emory University. He has worked at the U.S. Centers for Disease Control and Prevention (CDC) and the National Opinion Research Center (NORC) of the University of Chicago; and taught at Hokkaido University and the University of South Carolina. Hoffmann is the author of more than 100 journal articles and book chapters and 10 books on applied statistics, criminology, and the sociology of religion.
Descriere
This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.