• Produktbild: Regression and Other Stories
  • Produktbild: Regression and Other Stories

Regression and Other Stories

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.09.2020

Verlag

Cambridge University Press

Seitenzahl

548

Maße (L/B/H)

24,8/19/3,5 cm

Gewicht

1060 g

Sprache

Englisch

ISBN

978-1-107-67651-0

Beschreibung

Rezension

'Gelman, Hill and Vehtari provide an introductory regression book that hits an amazing trifecta: it motivates regression using real data examples, provides the necessary (but not superfluous) theory, and gives readers tools to implement these methods in their own work. The scope is ambitious - including introductions to causal inference and measurement - and the result is a book that I not only look forward to teaching from, but also keeping around as a reference for my own work.' Elizabeth Tipton, Northwestern University

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.09.2020

Verlag

Cambridge University Press

Seitenzahl

548

Maße (L/B/H)

24,8/19/3,5 cm

Gewicht

1060 g

Sprache

Englisch

ISBN

978-1-107-67651-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

Die Leseprobe wird geladen.
  • Produktbild: Regression and Other Stories
  • Produktbild: Regression and Other Stories
  • Preface; Part I. Fundamentals: 1. Overview; 2. Data and measurement; 3. Some basic methods in mathematics and probability; 4. Statistical inference; 5. Simulation; Part II. Linear Regression: 6. Background on regression modeling; 7. Linear regression with a single predictor; 8. Fitting regression models; 9. Prediction and Bayesian inference; 10. Linear regression with multiple predictors; 11. Assumptions, diagnostics, and model evaluation; 12. Transformations and regression; Part III. Generalized Linear Models: 13. Logistic regression; 14. Working with logistic regression; 15. Other generalized linear models; Part IV. Before and After Fitting a Regression: 16. Design and sample size decisions; 17. Poststratification and missing-data imputation; Part V. Causal Inference: 18. Causal inference and randomized experiments; 19. Causal inference using regression on the treatment variable; 20. Observational studies with all confounders assumed to be measured; 21. Additional topics in causal inference; Part VI. What Comes Next?: 22. Advanced regression and multilevel models; Appendices: A. Computing in R; B. 10 quick tips to improve your regression modelling; References; Author index; Subject index.