{"product_id":"paleontological-data-analysis-isbn-9781119933939","title":"Paleontological Data Analysis","description":"\u003cb\u003ePALEONTOLOGICAL DATA ANALYSIS\u003c\/b\u003e \u003cp\u003e \u003cb\u003eAn up-to-date edition of the indispensable guide to analysing paleontological data \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePaleontology 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, \u003ci\u003ePaleontological Data Analysis \u003c\/i\u003ehas 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.  \u003c\/p\u003e\u003cp\u003eReaders of the second edition of \u003ci\u003ePaleontological Data Analysis \u003c\/i\u003ereaders will also find:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eNew sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more \u003c\/li\u003e\n\u003cli\u003eNew use cases and examples using PAST, R, and Python software packages \u003c\/li\u003e\n\u003cli\u003eFull color illustrations throughout \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePaleontological Data Analysis \u003c\/i\u003eis ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields. \u003c\/p\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgements xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The nature of paleontological data 1\u003c\/p\u003e \u003cp\u003e1.2 Advantages and pitfalls of paleontological data analysis 5\u003c\/p\u003e \u003cp\u003e1.3 Software 7\u003c\/p\u003e \u003cp\u003eReferences 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Statistical concepts 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The population and the sample 9\u003c\/p\u003e \u003cp\u003e2.2 The frequency distribution of the population 9\u003c\/p\u003e \u003cp\u003e2.3 The normal distribution 11\u003c\/p\u003e \u003cp\u003e2.4 Cumulative probability 12\u003c\/p\u003e \u003cp\u003e2.5 The statistical sample, estimation of distribution parameters 14\u003c\/p\u003e \u003cp\u003e2.6 Null hypothesis significance testing 16\u003c\/p\u003e \u003cp\u003e2.7 Bayesian inference 20\u003c\/p\u003e \u003cp\u003e2.8 Exploratory data analysis 22\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Introduction to data visualization 24\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Graphic design principles 24\u003c\/p\u003e \u003cp\u003e3.2 Line charts 25\u003c\/p\u003e \u003cp\u003e3.3 Scatter plots 26\u003c\/p\u003e \u003cp\u003e3.4 Histograms 26\u003c\/p\u003e \u003cp\u003e3.5 Bar chart, box, and violin plots 29\u003c\/p\u003e \u003cp\u003e3.6 Normal probability plot 29\u003c\/p\u003e \u003cp\u003e3.7 Pie charts 31\u003c\/p\u003e \u003cp\u003e3.8 Ternary plots 32\u003c\/p\u003e \u003cp\u003e3.9 Heat maps, 3D plots, and Geographic Information System 33\u003c\/p\u003e \u003cp\u003e3.10 Plotting with R and Python 33\u003c\/p\u003e \u003cp\u003eReferences 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Univariate and bivariate statistical methods 38\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Parameter estimation and confidence intervals 38\u003c\/p\u003e \u003cp\u003e4.2 Testing for distribution 40\u003c\/p\u003e \u003cp\u003e4.3 Two-sample tests 43\u003c\/p\u003e \u003cp\u003e4.4 Multiple-sample tests 52\u003c\/p\u003e \u003cp\u003e4.5 Correlation 58\u003c\/p\u003e \u003cp\u003e4.6 Bivariate linear regression 64\u003c\/p\u003e \u003cp\u003e4.7 Generalized linear models 70\u003c\/p\u003e \u003cp\u003e4.8 Polynomial and nonlinear regression 73\u003c\/p\u003e \u003cp\u003e4.9 Mixture analysis 74\u003c\/p\u003e \u003cp\u003e4.10 Counts and contingency tables 76\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Introduction to multivariate data analysis 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Multivariate distributions 82\u003c\/p\u003e \u003cp\u003e5.2 Parametric multivariate tests – Hotelling’s T 2 82\u003c\/p\u003e \u003cp\u003e5.3 Nonparametric multivariate tests – permutation test 85\u003c\/p\u003e \u003cp\u003e5.4 Hierarchical cluster analysis 86\u003c\/p\u003e \u003cp\u003e5.5 K-means and k-medoids cluster analysis 92\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Morphometrics 96\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 The allometric equation 97\u003c\/p\u003e \u003cp\u003e6.2 Principal components analysis 101\u003c\/p\u003e \u003cp\u003e6.3 Multivariate allometry 108\u003c\/p\u003e \u003cp\u003e6.4 Linear discriminant analysis 112\u003c\/p\u003e \u003cp\u003e6.5 Multivariate analysis of variance 116\u003c\/p\u003e \u003cp\u003e6.6 Fourier shape analysis in polar coordinates 116\u003c\/p\u003e \u003cp\u003e6.7 Elliptic Fourier analysis 119\u003c\/p\u003e \u003cp\u003e6.8 Hangle Fourier analysis 122\u003c\/p\u003e \u003cp\u003e6.9 Eigenshape analysis 123\u003c\/p\u003e \u003cp\u003e6.10 Landmarks and size measures 125\u003c\/p\u003e \u003cp\u003e6.11 Procrustes fitting 127\u003c\/p\u003e \u003cp\u003e6.12 PCA of landmark data 130\u003c\/p\u003e \u003cp\u003e6.13 Thin-plate spline deformations 132\u003c\/p\u003e \u003cp\u003e6.14 Principal and partial warps 136\u003c\/p\u003e \u003cp\u003e6.15 Relative warps 139\u003c\/p\u003e \u003cp\u003e6.16 Regression of warp scores 141\u003c\/p\u003e \u003cp\u003e6.17 Common allometric component analysis 142\u003c\/p\u003e \u003cp\u003e6.18 Landmarks in 3D 143\u003c\/p\u003e \u003cp\u003e6.19 Disparity measures 144\u003c\/p\u003e \u003cp\u003e6.20 Morphogroup identification with machine learning 146\u003c\/p\u003e \u003cp\u003e6.21 Case study: the ontogeny of a Silurian trilobite 153\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Directional and spatial data analysis 162\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Analysis of directions and orientations in 2D 162\u003c\/p\u003e \u003cp\u003e7.2 Analysis of directions and orientations in 3D 164\u003c\/p\u003e \u003cp\u003e7.3 Spatial point pattern analysis 166\u003c\/p\u003e \u003cp\u003eReferences 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Analysis of tomographic and 3D-scan data 174\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 The technology of x-ray tomography 174\u003c\/p\u003e \u003cp\u003e8.2 Processing of volume data 175\u003c\/p\u003e \u003cp\u003e8.3 Functional morphology with 3D data 180\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Estimating paleobiodiversity 184\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Species richness estimation 185\u003c\/p\u003e \u003cp\u003e9.2 Rarefaction and related methods 187\u003c\/p\u003e \u003cp\u003e9.3 Diversity curves, origination, and extinction rates 192\u003c\/p\u003e \u003cp\u003e9.4 Abundance-based biodiversity indices 196\u003c\/p\u003e \u003cp\u003e9.5 Taxonomic distinctness 202\u003c\/p\u003e \u003cp\u003e9.6 Comparison of diversity indices 207\u003c\/p\u003e \u003cp\u003e9.7 Abundance models 208\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Paleoecology and paleobiogeography 216\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Paleobiogeography 216\u003c\/p\u003e \u003cp\u003e10.2 Paleoecology 217\u003c\/p\u003e \u003cp\u003e10.3 Association similarity indices for presence-absence data 219\u003c\/p\u003e \u003cp\u003e10.4 Association similarity indices for abundance data 223\u003c\/p\u003e \u003cp\u003e10.5 ANOSIM and PerMANOVA 228\u003c\/p\u003e \u003cp\u003e10.6 Principal coordinates analysis 229\u003c\/p\u003e \u003cp\u003e10.7 Non-metric multidimensional scaling 232\u003c\/p\u003e \u003cp\u003e10.8 Correspondence analysis 236\u003c\/p\u003e \u003cp\u003e10.9 Detrended correspondence analysis 240\u003c\/p\u003e \u003cp\u003e10.10 Seriation 242\u003c\/p\u003e \u003cp\u003e10.11 Nonlinear dimensionality reduction 245\u003c\/p\u003e \u003cp\u003e10.12 Canonical correspondence analysis 248\u003c\/p\u003e \u003cp\u003e10.13 Indicator species 251\u003c\/p\u003e \u003cp\u003e10.14 Network analysis 252\u003c\/p\u003e \u003cp\u003e10.15 Size-frequency and survivorship curves 254\u003c\/p\u003e \u003cp\u003e10.16 Case study: Devonian paleobiogeography 256\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Calibration – estimating paleoenvironments 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Modern analog technique 263\u003c\/p\u003e \u003cp\u003e11.2 Weighted averaging 265\u003c\/p\u003e \u003cp\u003e11.3 Weighted averaging partial least squares 267\u003c\/p\u003e \u003cp\u003e11.4 Which calibration method? 269\u003c\/p\u003e \u003cp\u003e11.5 Case study: Late Holocene temperature inferred from chironomids 271\u003c\/p\u003e \u003cp\u003eReferences 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Time series analysis 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Spectral analysis 274\u003c\/p\u003e \u003cp\u003e12.2 Wavelet analysis 282\u003c\/p\u003e \u003cp\u003e12.3 Autocorrelation 284\u003c\/p\u003e \u003cp\u003e12.4 Cross-correlation 287\u003c\/p\u003e \u003cp\u003e12.5 Runs test 290\u003c\/p\u003e \u003cp\u003e12.6 Time Series Trends and Regression 291\u003c\/p\u003e \u003cp\u003e12.7 Smoothing and filtering 293\u003c\/p\u003e \u003cp\u003eReferences 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Quantitative biostratigraphy 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Zonation of a single section 299\u003c\/p\u003e \u003cp\u003e13.2 Confidence intervals on stratigraphic ranges 301\u003c\/p\u003e \u003cp\u003e13.3 Regional and global biostratigraphic correlation 304\u003c\/p\u003e \u003cp\u003e13.4 Age models 330\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Phylogenetic analysis 338\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A dictionary of cladistics 338\u003c\/p\u003e \u003cp\u003e14.2 Parsimony analysis 339\u003c\/p\u003e \u003cp\u003e14.3 Characters 341\u003c\/p\u003e \u003cp\u003e14.4 Algorithms for Parsimony Analysis 342\u003c\/p\u003e \u003cp\u003e14.5 Character state reconstruction 347\u003c\/p\u003e \u003cp\u003e14.6 Evaluation of characters and trees 348\u003c\/p\u003e \u003cp\u003e14.7 Case study: the systematics of heterosporous ferns 355\u003c\/p\u003e \u003cp\u003e14.8 Other methods for phylogenetic analysis 359\u003c\/p\u003e \u003cp\u003e14.9 Phylogenetic Comparative Methods 362\u003c\/p\u003e \u003cp\u003eReferences 368\u003c\/p\u003e \u003cp\u003eIndex 371\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eØyvind Hammer, PhD, \u003c\/b\u003eis Professor of Paleontology at the University of Oslo, Norway. He has published very widely on paleontological subjects, and is co-author of the paleontological data analysis software PAST.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDavid A.T. Harper, DSc, \u003c\/b\u003eis Emeritus Professor of Paleontology at Durham University, UK. He has published extensively, including numerous monographs and textbooks, and developed the software PAST along with Øyvind Hammer.   \u003c\/p\u003e\u003cp\u003e \u003cb\u003eAn up-to-date edition of the indispensable guide to analysing paleontological data \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePaleontology 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, \u003ci\u003ePaleontological Data Analysis \u003c\/i\u003ehas 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.  \u003c\/p\u003e\u003cp\u003eReaders of the second edition of \u003ci\u003ePaleontological Data Analysis \u003c\/i\u003ereaders will also find:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eNew sections on machine learning, Bayesian inference, phylogenetic comparative methods, analysis of CT data, and much more \u003c\/li\u003e\n\u003cli\u003eNew use cases and examples using PAST, R, and Python software packages \u003c\/li\u003e\n\u003cli\u003eFull color illustrations throughout \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePaleontological Data Analysis \u003c\/i\u003eis ideal for paleontologists, evolutionary biologists, taxonomists, and students in any of these fields.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989748072677,"sku":"NP9781119933939","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119933939.jpg?v=1761785337","url":"https:\/\/k12savings.com\/es\/products\/paleontological-data-analysis-isbn-9781119933939","provider":"K12savings","version":"1.0","type":"link"}