A termék adatai:
| ISBN13: | 9781009282277 |
| ISBN10: | 1009282271 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 555 oldal |
| Nyelv: | angol |
| 700 |
Témakör:
A Practical Guide to Data Analysis Using R
An Example-Based Approach
Kiadó: Cambridge University Press
Megjelenés dátuma: 2024. május 31.
Normál ár:
Kiadói listaár:
GBP 69.99
GBP 69.99
Az Ön ára:
27 044 (25 756 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 6 761 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
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Rövid leírás:
Examples from diverse areas of statistical application demonstrate the use of R for data analysis and associated graphics.
Hosszú leírás:
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.
'A Practical Guide to Data Analysis Using R is an unusually rich and practical resource for data analysts. It gives coverage to important classical and modern methods of data analysis, while modeling a statistician's thinking and workflow using a wide range of real-world examples. It has broad appeal and application.' Sue Finch, University of Melbourne
'A Practical Guide to Data Analysis Using R is an unusually rich and practical resource for data analysts. It gives coverage to important classical and modern methods of data analysis, while modeling a statistician's thinking and workflow using a wide range of real-world examples. It has broad appeal and application.' Sue Finch, University of Melbourne
Tartalomjegyzék:
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.
