John H. Maindonald (Statistics Research Associates, W. John Braun (University of British Columbia, Jeffrey L. Andrews (University of British Columbia

Practical Guide to Data Analysis Using R (eBook, PDF)

An Example-Based Approach

• Format: PDF

Produktbeschreibung

Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 23. Mai 2024
• ISBN-13: 9781009282260
• Artikelnr.: 72349837

Autorenporträt

John H. Maindonald is Contract Associate at Statistics Research Associates and was previously Visiting Fellow at the Australian National University. He has had wide experience both as a university lecturer and as a quantitative problem solver, working with researchers in diverse areas. He is the author of 'Statistical Computation' (1984), and the senior author of 'Data Analysis and Graphics Using R' (third edition, 2010).

Inhaltsangabe

1. Learning from data, and tools for the task
2. Generalizing from models
3. Multiple linear regression
4. Exploiting the linear model framework
5. Generalized linear models and survival analysis
6. Time series models
7. Multilevel models, and repeated measures
8. Tree-based classification and regression
9. Multivariate data exploration and discrimination
Epilogue
A. The R system - a brief overview
References
References to R packages
Index of R functions
Subject index.