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1. |
Record Nr. |
UNINA9910842401203321 |
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Autore |
Hammer Øyvind |
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Titolo |
Paleontological Data Analysis |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2024 |
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©2024 |
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ISBN |
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1-119-93396-X |
1-119-93394-3 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (391 pages) |
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Altri autori (Persone) |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Introduction -- 1.1 The nature of paleontological data -- 1.1.1 Univariate measurements -- 1.1.2 Bivariate measurements -- 1.1.3 Multivariate morphometric measurements -- 1.1.4 Character matrices for phylogenetic analysis -- 1.1.5 Paleoecology and paleobiogeography - taxa in samples -- 1.1.6 Time series -- 1.1.7 Biostratigraphic data -- 1.2 Advantages and pitfalls of paleontological data analysis -- 1.2.1 Data analysis for the sake of it -- 1.2.2 The Texas sharpshooter -- 1.2.3 Explorative method or hypothesis testing? -- 1.2.4 Incomplete data -- 1.2.5 Statistical assumptions -- 1.2.6 Statistical and biological significance -- 1.2.7 Circularity -- 1.3 Software -- References -- Chapter 2 Statistical concepts -- 2.1 The population and the sample -- 2.2 The frequency distribution of the population -- 2.3 The normal distribution -- 2.4 Cumulative probability -- 2.5 The statistical sample, estimation of distribution parameters -- 2.6 Null hypothesis significance testing -- 2.6.1 Type I and type II errors -- 2.6.2 Power -- 2.6.3 Robustness -- 2.6.4 Effect size -- 2.6.5 NHST misunderstandings -- 2.7 Bayesian inference -- 2.7.1 Bayes' theorem -- 2.7.2 Markov Chain Monte Carlo -- 2.7.3 What is the point? -- 2.7.4 Bayes factors -- 2.8 Exploratory data analysis -- References -- Chapter 3 Introduction to data visualization -- 3.1 Graphic design principles -- 3.1.1 Vector graphics |
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-- 3.1.2 Fonts -- 3.1.3 Colors -- 3.1.4 Fills -- 3.2 Line charts -- 3.3 Scatter plots -- 3.4 Histograms -- 3.5 Bar chart, box, and violin plots -- 3.6 Normal probability plot -- 3.7 Pie charts -- 3.8 Ternary plots -- 3.9 Heat maps, 3D plots, and Geographic Information System -- 3.10 Plotting with R and Python -- References -- Chapter 4 Univariate and bivariate statistical methods. |
4.1 Parameter estimation and confidence intervals -- 4.1.1 Bootstrapping -- 4.1.2 Credible intervals -- 4.2 Testing for distribution -- 4.2.1 Shapiro-Wilk test for normal distribution -- 4.3 Two-sample tests -- 4.3.1 Student's t test for the equality of means -- 4.3.2 F test for the equality of variances -- 4.3.3 Mann-Whitney U test for equality of position -- 4.3.4 Kolmogorov-Smirnov test for equality of distribution -- 4.3.5 Permutation tests -- 4.4 Multiple-sample tests -- 4.4.1 One-way ANOVA -- 4.4.2 Kruskal-Wallis test -- 4.5 Correlation -- 4.5.1 Linear correlation -- 4.5.2 Non-parametric correlation -- 4.6 Bivariate linear regression -- 4.6.1 Ordinary least-squares linear regression -- 4.6.2 Reduced major axis regression -- 4.7 Generalized linear models -- 4.7.1 GLM regression of counts -- 4.7.2 GLM regression of percentages or proportions -- 4.7.3 GLM regression of binary data (logistic regression) -- 4.8 Polynomial and nonlinear regression -- 4.8.1 Akaike information criterion -- 4.9 Mixture analysis -- 4.10 Counts and contingency tables -- References -- Chapter 5 Introduction to multivariate data analysis -- 5.1 Multivariate distributions -- 5.2 Parametric multivariate tests - Hotelling's T2 -- 5.3 Nonparametric multivariate tests - permutation test -- 5.4 Hierarchical cluster analysis -- 5.5 K-means and k-medoids cluster analysis -- References -- Chapter 6 Morphometrics -- 6.1 The allometric equation -- 6.2 Principal components analysis -- 6.2.1 Transformation and normalization -- 6.2.2 Relative importance of principal components -- 6.2.3 Algorithms for PCA -- 6.2.4 PCA is not hypothesis testing -- 6.2.5 Factor analysis -- 6.3 Multivariate allometry -- 6.4 Linear discriminant analysis -- 6.4.1 Discriminant analysis for more than two groups -- 6.5 Multivariate analysis of variance -- 6.6 Fourier shape analysis in polar coordinates. |
6.7 Elliptic Fourier analysis -- 6.8 Hangle Fourier analysis -- 6.9 Eigenshape analysis -- 6.10 Landmarks and size measures -- 6.10.1 Sliding landmarks -- 6.10.2 Size from landmarks -- 6.10.3 Landmark registration and shape coordinates -- 6.11 Procrustes fitting -- 6.12 PCA of landmark data -- 6.13 Thin-plate spline deformations -- 6.14 Principal and partial warps -- 6.14.1 The affine (uniform) component -- 6.14.2 Partial warp scores as shape coordinates -- 6.15 Relative warps -- 6.16 Regression of warp scores -- 6.17 Common allometric component analysis -- 6.18 Landmarks in 3D -- 6.19 Disparity measures -- 6.19.1 Morphometric disparity measures -- 6.19.2 Disparity measures from discrete characters -- 6.19.3 Sampling effects and rarefaction -- 6.19.4 Morphospaces -- 6.20 Morphogroup identification with machine learning -- 6.20.1 K-nearest-neighbor classification -- 6.20.2 Naïve Bayes -- 6.20.3 Decision trees and random forests -- 6.20.4 Neural networks -- 6.20.5 Image classification and convolutional neural networks -- 6.21 Case study: the ontogeny of a Silurian trilobite -- 6.21.1 Size -- 6.21.2 Distance measurements and allometry -- 6.21.3 Procrustes fitting of landmarks -- 6.21.4 Common allometric component analysis -- References -- Chapter 7 Directional and spatial data analysis -- 7.1 Analysis of directions and orientations in 2D -- 7.1.1 Plotting circular data -- 7.1.2 Testing for preferred direction -- 7.2 Analysis of directions and orientations in 3D -- 7.3 Spatial point pattern analysis -- 7.3.1 Nearest-neighbor analysis -- 7.3.2 Ripley's K analysis -- 7.3.3 |
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Correlation length analysis -- References -- Chapter 8 Analysis of tomographic and 3D-scan data -- 8.1 The technology of x-ray tomography -- 8.2 Processing of volume data -- 8.2.1 Volumes and surface meshes -- 8.2.2 Segmentation -- 8.2.3 Landmarks from CT data. |
8.2.4 Analysis of volume data -- 8.3 Functional morphology with 3D data -- 8.3.1 Structural analysis - stresses and strains -- 8.3.2 Computational fluid dynamics -- References -- Chapter 9 Estimating paleobiodiversity -- 9.1 Species richness estimation -- 9.1.1 Species richness estimation from single-sample abundance data -- 9.1.2 Species richness estimation from multiple-sample presence-absence data -- 9.2 Rarefaction and related methods -- 9.2.1 Classical rarefaction -- 9.2.2 Unconditional variance rarefaction -- 9.2.3 Shareholder quorum subsampling -- 9.2.4 Sample rarefaction -- 9.3 Diversity curves, origination, and extinction rates -- 9.4 Abundance-based biodiversity indices -- 9.4.1 Confidence intervals for abundance-based diversity indices -- 9.4.2 Rarefaction of abundance-based diversity indices -- 9.5 Taxonomic distinctness -- 9.6 Comparison of diversity indices -- 9.7 Abundance models -- References -- Chapter 10 Paleoecology and paleobiogeography -- 10.1 Paleobiogeography -- 10.2 Paleoecology -- 10.3 Association similarity indices for presence-absence data -- 10.4 Association similarity indices for abundance data -- 10.5 ANOSIM and PerMANOVA -- 10.6 Principal coordinates analysis -- 10.6.1 Metric distance measures and the triangle inequality -- 10.7 Non-metric multidimensional scaling -- 10.8 Correspondence analysis -- 10.9 Detrended correspondence analysis -- 10.10 Seriation -- 10.11 Nonlinear dimensionality reduction -- 10.11.1 ISOMAP -- 10.11.2 Spectral embedding -- 10.11.3 UMAP -- 10.12 Canonical correspondence analysis -- 10.13 Indicator species -- 10.14 Network analysis -- 10.15 Size-frequency and survivorship curves -- 10.16 Case study: Devonian paleobiogeography -- References -- Chapter 11 Calibration - estimating paleoenvironments -- 11.1 Modern analog technique -- 11.2 Weighted averaging. |
11.3 Weighted averaging partial least squares -- 11.4 Which calibration method? -- 11.5 Case study: Late Holocene temperature inferred from chironomids -- References -- Chapter 12 Time series analysis -- 12.1 Spectral analysis -- 12.1.1 Discrete Fourier transform -- 12.1.2 Spectral analysis with the REDFIT procedure -- 12.1.3 Spectral analysis with the multitaper method -- 12.1.4 Evolutive spectral analysis -- 12.2 Wavelet analysis -- 12.3 Autocorrelation -- 12.4 Cross-correlation -- 12.5 Runs test -- 12.6 Time Series Trends and Regression -- 12.6.1 Mann-Kendall trend test -- 12.6.2 Regression in the presence of autocorrelation -- 12.7 Smoothing and filtering -- 12.7.1 Moving average -- 12.7.2 Exponential moving average -- 12.7.3 Moving median -- 12.7.4 Non-local means -- 12.7.5 FIR filtering -- 12.7.6 Fitting to models -- References -- Chapter 13 Quantitative biostratigraphy -- 13.1 Zonation of a single section -- 13.1.1 Stratigraphically constrained clustering -- 13.2 Confidence intervals on stratigraphic ranges -- 13.2.1 Parametric confidence intervals on stratigraphic ranges -- 13.2.2 Non-parametric confidence intervals on stratigraphic ranges -- 13.3 Regional and global biostratigraphic correlation -- 13.3.1 Graphic correlation -- 13.3.2 Constrained optimization -- 13.3.3 Ranking and scaling -- 13.3.4 Normality testing and variance analysis -- 13.3.5 Correlation (CASC) -- 13.3.6 Unitary Associations -- 13.3.7 Biostratigraphy by ordination -- 13.3.8 What is the best method for biostratigraphic correlation? -- 13.4 Age models -- 13.4.1 Simple interpolation -- 13.4.2 Simple regression and smoothing -- 13.4.3 Classical age models with Monte Carlo |
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simulation -- 13.4.4 Bayesian age modeling -- References -- Chapter 14 Phylogenetic analysis -- 14.1 A dictionary of cladistics -- 14.2 Parsimony analysis -- 14.3 Characters. |
14.4 Algorithms for Parsimony Analysis. |
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2. |
Record Nr. |
UNINA9910961436203321 |
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Autore |
Foley John Miles |
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Titolo |
Oral tradition and the internet : pathways of the mind / / John Miles Foley |
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Pubbl/distr/stampa |
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Urbana, : University of Illinois Press, 2012 |
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ISBN |
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9780252094309 |
0252094301 |
9781283992565 |
1283992566 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (313 pages) |
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Disciplina |
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Soggetti |
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Folklore and the Internet |
Oral tradition - Computer network resources |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [273]-285) and index. |
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Nota di contenuto |
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""Cover""; ""Title Page""; ""Copyright Page""; ""Table of Nodes""; ""Preface""; ""For Book-readers Only""; ""Home Page""; ""Getting Started""; ""Disclaimer""; ""Book versus Website""; ""Response""; ""Linkmaps""; ""Nodes in Alphabetical Order""; ""A Foot in Each World""; ""Accuracy""; ""Agora As Verbal Marketplace""; ""Agora Correspondences""; ""Agoraphobia""; ""Arena of Oral Tradition""; ""Arena of Text""; ""Arena of the Web""; ""Audience Critique""; ""Bellerophon and His Tablet""; ""Citizenship in Multiple Agoras""; ""Cloud and Tradition""; ""Contingency""; ""Culture As Network"" |
""Culture Shock""""Distributed Authorship""; ""Don't Trust Everything You Read in Books""; ""eAgora""; ""eCompanions""; ""eEditions""; ""ePathways""; ""eWords""; ""Excavating an Epic""; ""Freezing Wikipedia""; ""Getting Published or Getting Sequestered""; ""Homo Sapiens' Calendar Year""; ""How to Build a Book""; ""Ideology of the Text""; ""Illusion of |
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Object""; ""Illusion of Stasis""; ""Impossibility of tPathways""; ""In the Public Domain""; ""Indigestible Words""; ""Just the Facts""; ""Leapfrogging the Text""; ""Misnavigation""; ""Morphing Book""; ""Museum of Verbal Art"" |
""Not So Willy-nilly""""oAgora""; ""Online with OT""; ""oPathways""; ""Owning versus Sharing""; ""oWords""; ""Polytaxis""; ""Proverbs""; ""Reading Backwards""; ""Real-time versus Asynchronous""; ""Reality Remains in Play""; ""Recur Not Repeat""; ""Remix""; ""Responsible Agora-business""; ""Resynchronizing the Event""; ""Singing on the Page""; ""Spectrum of Texts""; ""Stories Are Linkmaps""; ""tAgora""; ""Texts and Intertextuality""; ""Three Agoras""; ""tWords""; ""Variation within Limits""; ""Why Not Textualize""; ""Wiki""; ""Further Reading""; ""Notes""; ""Index"" |
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Sommario/riassunto |
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This title illustrates and explains the fundamental similarities and correspondences between humankind's oldest and newest thought-technologies: oral tradition and the Internet. |
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3. |
Record Nr. |
UNISALENTO991004383838207536 |
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Autore |
Zachariä von Lingenthal, Karl Salomon |
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Titolo |
Corso di diritto civile francese / per C.S. Zachariae ; tradotto dal tedesco sulla 5. ed. (1839) e riveduto ed acresciuto col consenso dell'A. da Aubry e Rau ; nuova versione italiana con note per Luigi Lo Gatto |
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Pubbl/distr/stampa |
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Napoli : Rondinella, 1851- |
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Descrizione fisica |
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3 v. in 1 (336, 444, 333 p.) ; 28 cm |
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Altri autori (Persone) |
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Aubry, Charles |
Rau, Charles-Frèdèric |
Lo Gatto, Luigi |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sul front.: Seconda ed. francese (1850), aggiuntovi il testo delle leggi che àn modificata la legislazione nel Belgio, e la giurisprudenza delle Corti belgiche |
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4. |
Record Nr. |
UNINA9911018891403321 |
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Autore |
Pease Gene <1950-> |
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Titolo |
Human capital analytics : how to harness the potential of your organization's greatest asset / / Gene Pease, Boyce Byerly, Jac Fitz-enz |
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Pubbl/distr/stampa |
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Hoboken, N.J., : Wiley, c2013 |
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ISBN |
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9781119205050 |
1119205050 |
9781283665087 |
1283665085 |
9781118506974 |
1118506979 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (252 p.) |
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Collana |
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Wiley & SAS business series |
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Altri autori (Persone) |
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ByerlyBoyce <1962-> |
Fitz-enzJac |
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Disciplina |
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Soggetti |
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Human capital |
Personnel management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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""Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset""; ""Copyright""; ""Contents""; ""Preface""; ""Acknowledgments""; ""Introduction Realizing the Dream: From Nuisance to Necessity""; ""Starting from the Back Row""; ""The Value Dream""; ""Barriers in the Human Resource Area""; ""Organizations are all about People, Not Things""; ""Managing Risk""; ""Historic Fundamentals""; ""Intangibles""; ""Predictability""; ""Need for Definition""; ""Breakthrough""; ""Practicality""; ""Analytics Model Foundation""; ""Awakening""; ""Notes"" |
""Chapter 1: Human Capital Analytics""""Human Capital Analytics Continuum""; ""Summary""; ""Notes""; ""Chapter 2: Alignment""; ""The Stakeholder Workshop: Creating the Right Climate for Alignment""; ""Aligning Stakeholders""; ""Who Are Your Stakeholders?""; ""What Should You Accomplish in a Stakeholder Meeting?""; ""Deciding What to Measure with Your Stakeholders""; ""Leading Indicators""; ""Business Impact""; ""Assigning Financial Values to ""Intangibles""""; ""Defining |
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Your Participants""; ""Summary""; ""Notes""; ""Chapter 3: The Measurement Plan""; ""Defining the Intervention(s)"" |
""Measurement Map""""Hypotheses or Business Questions""; ""Defining the Metrics""; ""Demographics""; ""Data Sources and Requirements""; ""Summary""; ""Note""; ""Chapter 4: It's All about the Data""; ""Types of Data""; ""Tying Your Data Sets Together""; ""Difficulties in Obtaining Data""; ""Ethics of Measurement and Evaluation""; ""Telling the Truth""; ""Summary""; ""Notes""; ""Chapter 5: What Dashboards Are Telling You: Descriptive Statistics and Correlations""; ""Descriptive Statistics""; ""Going Graphic with the Data""; ""Data over Time""; ""Descriptive Statistics on Steroids"" |
""Correlation Does Not Imply Causation""""Summary""; ""Notes""; ""Chapter 6: Causation: What Really Drives Performance""; ""Can You Create Separate Test and Control Groups?""; ""Are There Observable Differences?""; ""Did You Consider Prior Performance?""; ""Did You Consider Time-Related Changes?""; ""Did You Look at the Descriptive Statistics?""; ""Have You Considered the Relationship between the Metrics?""; ""A Gentle Introduction to Statistics""; ""Basic Ideas behind Regression""; ""Model Fit and Statistical Significance""; ""Summary""; ""Notes""; ""Chapter 7: Beyond ROI to Optimization"" |
""Optimization""""Segmentation""; ""Mixture""; ""Saturation""; ""Metric Interaction""; ""Time Line""; ""Summary""; ""Notes""; ""Chapter 8: Share the Story""; ""Presenting the Financials""; ""Telling the Story and Adding Up the Numbers""; ""Preparing for the Meetings""; ""Summary""; ""Notes""; ""Chapter 9: Conclusion""; ""Human Capital Analytics""; ""Alignment""; ""The Measurement Plan""; ""It's All about the Data""; ""What Dashboards Are Telling You: Descriptive Statistics and Correlations""; ""Causation: What Really Drives Performance""; ""Beyond ROI to Optimization""; ""The Ultimate Goal"" |
""What Others Think about the Future of Analytics"" |
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Sommario/riassunto |
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An insightful look at the implementation of advanced analytics on human capital Human capital analytics, also known as human resources analytics or talent analytics, is the application of sophisticated data mining and business analytics techniques to human resources data. Human Capital Analytics provides an in-depth look at the science of human capital analytics, giving practical examples from case studies of companies applying analytics to their people decisions and providing a framework for using predictive analytics to optimize human capital investments. Written by Gene Pease, Boyce Byerly, |
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