Paleontological Data Analysis
| AUTHOR | Hammer, ØYvind; Harper, David A. T.; Hammer, yvind et al. |
| PUBLISHER | Wiley (03/04/2024) |
| PRODUCT TYPE | Hardcover (Hardcover) |
An up-to-date edition of the indispensable guide to analysing paleontological data
Paleontology has developed in recent decades into an increasingly data-driven discipline, which brings to bear a huge variety of statistical tools. Applying statistical methods to paleontological data requires a discipline-specific understanding of which methods and parameters are the most appropriate ones, and how to account for statistical bias inherent in the fossil record. By guiding the reader to these and other fundamental questions in the statistical analysis of fossilized specimens, Paleontological Data Analysis has become the standard text for anyone with an interest in quantitative analysis of the fossil record. Now fully updated to reflect the latest statistical methods and disciplinary advances, it is an essential tool for practitioners and students alike.
Readers of the second edition of Paleontological Data Analysis readers will also find:
- New sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more
- New use cases and examples using PAST, R, and Python software packages
- Full color illustrations throughout
Paleontological Data Analysis is ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.
An up-to-date edition of the indispensable guide to analysing paleontological data
Paleontology has developed in recent decades into an increasingly data-driven discipline, which brings to bear a huge variety of statistical tools. Applying statistical methods to paleontological data requires a discipline-specific understanding of which methods and parameters are the most appropriate ones, and how to account for statistical bias inherent in the fossil record. By guiding the reader to these and other fundamental questions in the statistical analysis of fossilized specimens, Paleontological Data Analysis has become the standard text for anyone with an interest in quantitative analysis of the fossil record. Now fully updated to reflect the latest statistical methods and disciplinary advances, it is an essential tool for practitioners and students alike.
Readers of the second edition of Paleontological Data Analysis readers will also find:
- New sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more
- New use cases and examples using PAST, R, and Python software packages
- Full color illustrations throughout
Paleontological Data Analysis is ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.
An up-to-date edition of the indispensable guide to analysing paleontological data
Paleontology has developed in recent decades into an increasingly data-driven discipline, which brings to bear a huge variety of statistical tools. Applying statistical methods to paleontological data requires a discipline-specific understanding of which methods and parameters are the most appropriate ones, and how to account for statistical bias inherent in the fossil record. By guiding the reader to these and other fundamental questions in the statistical analysis of fossilized specimens, Paleontological Data Analysis has become the standard text for anyone with an interest in quantitative analysis of the fossil record. Now fully updated to reflect the latest statistical methods and disciplinary advances, it is an essential tool for practitioners and students alike.
Readers of the second edition of Paleontological Data Analysis readers will also find:
- New sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more
- New use cases and examples using PAST, R, and Python software packages
- Full color illustrations throughout
Paleontological Data Analysis is ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.