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Biblioteca

MARC Lista (tabellare)
Correspondence analysis in practice / Michael Greenacre
Correspondence analysis in practice / Michael Greenacre
Autore Greenacre, Michael
Edizione [2. ed]
Pubbl/distr/stampa Boca Raton [etc.], : Chapman & Hall, 2007
Descrizione fisica XIII, 280 p. : ill. ; 26 cm.
Disciplina 519.537
Collana Interdisciplinary statistics
Soggetto topico Analisi statistica
ISBN 1584886161
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISANNIO-MIL0732200
Greenacre, Michael  
Boca Raton [etc.], : Chapman & Hall, 2007
Materiale a stampa
Lo trovi qui: Univ. del Sannio
Opac: Controlla la disponibilità qui
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
Autore Beh Eric J.
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Descrizione fisica 1 online resource (243 pages) : illustrations
Disciplina 519.537
Collana Wiley Series in Probability and Statistics Ser.
Soggetto topico Correspondence analysis (Statistics)
Soggetto genere / forma Electronic books.
ISBN 1-119-04196-1
1-119-04448-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Data Visualisation -- 1.2 Correspondence Analysis in a "Nutshell" -- 1.3 Data Sets -- 1.3.1 Traditional European Food Data -- 1.3.2 Temperature Data -- 1.3.3 Shoplifting Data -- 1.3.4 Alligator Data -- 1.4 Symmetrical Versus Asymmetrical Association -- 1.5 Notation -- 1.5.1 The Two‐way Contingency Table -- 1.5.2 The Three‐way Contingency Table -- 1.6 Formal Test of Symmetrical Association -- 1.6.1 Test of Independence for Two‐way Contingency Tables -- 1.6.2 The Chi‐squared Statistic for a Two‐way Table -- 1.6.3 Analysis of the Traditional European Food Data -- 1.6.4 The Chi‐squared Statistic for a Three‐way Table -- 1.6.5 Analysis of the Alligator Data -- 1.7 Formal Test of Asymmetrical Association -- 1.7.1 Test of Predictability for Two‐way Contingency Tables -- 1.7.2 The Goodman-Kruskal tau Index -- 1.7.3 Analysis of the Traditional European Food Data -- 1.7.4 Test of Predictability for Three‐way Contingency Tables -- 1.7.5 Marcotorchino's Index -- 1.7.6 Analysis of the Alligator Data -- 1.7.7 The Gray-Williams Index and Delta Index -- 1.8 Correspondence Analysis and R -- 1.9 Overview of the Book -- Part I Classical Analysis of Two Categorical Variables -- Chapter 2 Simple Correspondence Analysis -- 2.1 Introduction -- 2.2 Reducing Multi‐dimensional Space -- 2.2.1 Profiles Cloud of Points -- 2.2.2 Profiles for the Traditional European Food Data -- 2.2.3 Weighted Centred Profiles -- 2.3 Measuring Symmetric Association -- 2.3.1 The Pearson Ratio -- 2.3.2 Analysis of the Traditional European Food Data -- 2.4 Decomposing the Pearson Residual for Nominal Variables -- 2.4.1 The Generalised SVD of γij−1 -- 2.4.2 SVD of the Pearson Ratio's -- 2.4.3 GSVD and the Traditional European Food Data -- 2.5 Constructing a Low‐Dimensional Display.
2.5.1 Standard Coordinates -- 2.5.2 Principal Coordinates -- 2.6 Practicalities of the Low‐Dimensional Plot -- 2.6.1 The Two‐Dimensional Correspondence Plot -- 2.6.2 What is NOT Being Shown in a Two‐Dimensional Correspondence Plot? -- 2.6.3 The Three‐Dimensional Correspondence Plot -- 2.7 The Biplot Display -- 2.7.1 Definition -- 2.7.2 Isometric Biplots of the Traditional European Food Data -- 2.7.3 What is NOT Being Shown in a Two‐Dimensional Biplot? -- 2.8 The Case for No Visual Display -- 2.9 Detecting Statistically Significant Points -- 2.9.1 Confidence Circles and Ellipses -- 2.9.2 Confidence Ellipses for the Traditional European Food Data -- 2.10 Approximate p‐values -- 2.10.1 The Hypothesis Test and its p‐value -- 2.10.2 P‐values and the Traditional European Food Data -- 2.11 Final Comments -- Chapter 3 Non‐Symmetrical Correspondence Analysis -- 3.1 Introduction -- 3.2 Quantifying Asymmetric Association -- 3.2.1 The Goodman-Kruskal tau Index -- 3.2.2 The τ Index and the Traditional European Food Data -- 3.2.3 Weighted Centred Column Profile -- 3.2.4 Profiles of the Traditional European Food Data -- 3.3 Decomposing πi|j for Nominal Variables -- 3.3.1 The Generalised SVD of πi|j -- 3.3.2 GSVD and the Traditional Food Data -- 3.4 Constructing a Low‐Dimensional Display -- 3.4.1 Standard Coordinates -- 3.4.2 Principal Coordinates -- 3.5 Practicalities of the Low‐Dimensional Plot -- 3.5.1 The Two‐Dimensional Correspondence Plot -- 3.5.2 The Three‐Dimensional Correspondence Plot -- 3.6 The Biplot Display -- 3.6.1 Definition -- 3.6.2 The Column Isometric Biplot for the Traditional Food Data -- 3.6.3 The Three‐Dimensional Biplot -- 3.7 Detecting Statistically Significant Points -- 3.7.1 Confidence Circles and Ellipses -- 3.7.2 Confidence Ellipses for the Traditional Food Data -- 3.8 Final Comments.
Part II Ordinal Analysis of Two Categorical Variables -- Chapter 4 Simple Ordinal Correspondence Analysis -- 4.1 Introduction -- 4.2 A Simple Correspondence Analysis of the Temperature Data -- 4.3 On the Mean and Variation of Profiles with Ordered Categories -- 4.3.1 Profiles of the Temperature Data -- 4.3.2 Defining Scores -- 4.3.3 On the Mean of the Profiles -- 4.3.4 On the Variation of the Profiles -- 4.3.5 Mean and Variation of Profiles for the Temperature Data -- 4.4 Decomposing the Pearson Residual for Ordinal Variables -- 4.4.1 The Bivariate Moment Decomposition of γij−1 -- 4.4.2 BMD and the Temperature Data -- 4.5 Constructing a Low‐Dimensional Display -- 4.5.1 Standard Coordinates -- 4.5.2 Principal Coordinates -- 4.5.3 Practicalities of the Ordered Principal Coordinates -- 4.6 The Biplot Display -- 4.6.1 Definition -- 4.6.2 Ordered Column Isometric Biplot -- 4.6.3 Ordered Row Isometric Biplot -- 4.6.4 Ordered Isometric Biplots for the Temperature Data -- 4.7 Final Comments -- Chapter 5 Ordered Non‐symmetrical Correspondence Analysis -- 5.1 Introduction -- 5.2 The Goodman-Kruskal tau Index Revisited -- 5.3 Decomposing πi|j for Ordinal and Nominal Variables -- 5.3.1 The Hybrid Decomposition of πi|j -- 5.3.2 Hybrid Decomposition and the Shoplifting Data -- 5.4 Constructing a Low‐Dimensional Display -- 5.4.1 Standard Coordinates -- 5.4.2 Principal Coordinates -- 5.5 The Biplot -- 5.5.1 An Overview -- 5.5.2 Column Isometric Biplot -- 5.5.3 Column Isometric Biplot of the Shoplifting Data -- 5.5.4 Row Isometric Biplot -- 5.5.5 Row Isometric Biplot of the Shoplifting Data -- 5.5.6 Distance Measures and the Row Isometric Biplots -- 5.6 Some Final Words -- Part III Analysis of Multiple Categorical Variables -- Chapter 6 Multiple Correspondence Analysis -- 6.1 Introduction -- 6.2 Crisp Coding and the Indicator Matrix -- 6.2.1 Crisp Coding.
6.2.2 The Indicator Matrix -- 6.2.3 Crisp Coding and the Alligator Data -- 6.2.4 Application of Multiple Correspondence Analysis using the Indicator Matrix -- 6.3 The Burt Matrix -- 6.4 Stacking -- 6.4.1 A Definition -- 6.4.2 Stacking and the Alligator Data - Lake(Size)× Food -- 6.4.3 Stacking and the Alligator Data - Food(Size)× Lake -- 6.5 Final Comments -- Chapter 7 Multi‐way Correspondence Analysis -- 7.1 An Introduction -- 7.2 Pearson's Residual γijk−1 and the Partition of X2 -- 7.2.1 The Pearson Residual -- 7.2.2 The Partition of X2 -- 7.2.3 Partition of X2 for the Alligator Data -- 7.3 Symmetric Multi‐way Correspondence Analysis -- 7.3.1 Tucker3 Decomposition of γijk−1 -- 7.3.2 T3D and the Analysis of Two Variables -- 7.3.3 On the Choice of the Number of Components -- 7.3.4 Tucker3 Decomposition of γijk−1 and the Alligator Data -- 7.4 Constructing a Low‐Dimensional Display -- 7.4.1 Principal Coordinates -- 7.4.2 The Interactive Biplot -- 7.4.3 Column‐Tube Interactive Biplot for the Alligator Data -- 7.4.4 Row Interactive Biplot for the Alligator Data -- 7.5 The Marcotorchino Residual πi|j,k and the Partition of τM -- 7.5.1 The Marcotrochino Residual -- 7.5.2 The Partition of τM -- 7.5.3 Partition of τM for the Alligator Data -- 7.6 Non‐symmetrical Multi‐way Correspondence Analysis -- 7.6.1 Tucker3 Decomposition of πi|j,k -- 7.6.2 Tucker3 Decomposition of πi|j,k and the Alligator Data -- 7.7 Constructing a Low‐Dimensional Display -- 7.7.1 On the Choice of Coordinates -- 7.7.2 Column-Tube Interactive Biplot for the Alligator Data -- 7.8 Final Comments -- References -- Author Index -- Subject Index -- EULA.
Record Nr. UNINA-9910555118003321
Beh Eric J.  
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
Autore Beh Eric J.
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Descrizione fisica 1 online resource (243 pages) : illustrations
Disciplina 519.537
Collana Wiley Series in Probability and Statistics
Soggetto topico Correspondence analysis (Statistics)
ISBN 1-119-04196-1
1-119-04448-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Data Visualisation -- 1.2 Correspondence Analysis in a "Nutshell" -- 1.3 Data Sets -- 1.3.1 Traditional European Food Data -- 1.3.2 Temperature Data -- 1.3.3 Shoplifting Data -- 1.3.4 Alligator Data -- 1.4 Symmetrical Versus Asymmetrical Association -- 1.5 Notation -- 1.5.1 The Two‐way Contingency Table -- 1.5.2 The Three‐way Contingency Table -- 1.6 Formal Test of Symmetrical Association -- 1.6.1 Test of Independence for Two‐way Contingency Tables -- 1.6.2 The Chi‐squared Statistic for a Two‐way Table -- 1.6.3 Analysis of the Traditional European Food Data -- 1.6.4 The Chi‐squared Statistic for a Three‐way Table -- 1.6.5 Analysis of the Alligator Data -- 1.7 Formal Test of Asymmetrical Association -- 1.7.1 Test of Predictability for Two‐way Contingency Tables -- 1.7.2 The Goodman-Kruskal tau Index -- 1.7.3 Analysis of the Traditional European Food Data -- 1.7.4 Test of Predictability for Three‐way Contingency Tables -- 1.7.5 Marcotorchino's Index -- 1.7.6 Analysis of the Alligator Data -- 1.7.7 The Gray-Williams Index and Delta Index -- 1.8 Correspondence Analysis and R -- 1.9 Overview of the Book -- Part I Classical Analysis of Two Categorical Variables -- Chapter 2 Simple Correspondence Analysis -- 2.1 Introduction -- 2.2 Reducing Multi‐dimensional Space -- 2.2.1 Profiles Cloud of Points -- 2.2.2 Profiles for the Traditional European Food Data -- 2.2.3 Weighted Centred Profiles -- 2.3 Measuring Symmetric Association -- 2.3.1 The Pearson Ratio -- 2.3.2 Analysis of the Traditional European Food Data -- 2.4 Decomposing the Pearson Residual for Nominal Variables -- 2.4.1 The Generalised SVD of γij−1 -- 2.4.2 SVD of the Pearson Ratio's -- 2.4.3 GSVD and the Traditional European Food Data -- 2.5 Constructing a Low‐Dimensional Display.
2.5.1 Standard Coordinates -- 2.5.2 Principal Coordinates -- 2.6 Practicalities of the Low‐Dimensional Plot -- 2.6.1 The Two‐Dimensional Correspondence Plot -- 2.6.2 What is NOT Being Shown in a Two‐Dimensional Correspondence Plot? -- 2.6.3 The Three‐Dimensional Correspondence Plot -- 2.7 The Biplot Display -- 2.7.1 Definition -- 2.7.2 Isometric Biplots of the Traditional European Food Data -- 2.7.3 What is NOT Being Shown in a Two‐Dimensional Biplot? -- 2.8 The Case for No Visual Display -- 2.9 Detecting Statistically Significant Points -- 2.9.1 Confidence Circles and Ellipses -- 2.9.2 Confidence Ellipses for the Traditional European Food Data -- 2.10 Approximate p‐values -- 2.10.1 The Hypothesis Test and its p‐value -- 2.10.2 P‐values and the Traditional European Food Data -- 2.11 Final Comments -- Chapter 3 Non‐Symmetrical Correspondence Analysis -- 3.1 Introduction -- 3.2 Quantifying Asymmetric Association -- 3.2.1 The Goodman-Kruskal tau Index -- 3.2.2 The τ Index and the Traditional European Food Data -- 3.2.3 Weighted Centred Column Profile -- 3.2.4 Profiles of the Traditional European Food Data -- 3.3 Decomposing πi|j for Nominal Variables -- 3.3.1 The Generalised SVD of πi|j -- 3.3.2 GSVD and the Traditional Food Data -- 3.4 Constructing a Low‐Dimensional Display -- 3.4.1 Standard Coordinates -- 3.4.2 Principal Coordinates -- 3.5 Practicalities of the Low‐Dimensional Plot -- 3.5.1 The Two‐Dimensional Correspondence Plot -- 3.5.2 The Three‐Dimensional Correspondence Plot -- 3.6 The Biplot Display -- 3.6.1 Definition -- 3.6.2 The Column Isometric Biplot for the Traditional Food Data -- 3.6.3 The Three‐Dimensional Biplot -- 3.7 Detecting Statistically Significant Points -- 3.7.1 Confidence Circles and Ellipses -- 3.7.2 Confidence Ellipses for the Traditional Food Data -- 3.8 Final Comments.
Part II Ordinal Analysis of Two Categorical Variables -- Chapter 4 Simple Ordinal Correspondence Analysis -- 4.1 Introduction -- 4.2 A Simple Correspondence Analysis of the Temperature Data -- 4.3 On the Mean and Variation of Profiles with Ordered Categories -- 4.3.1 Profiles of the Temperature Data -- 4.3.2 Defining Scores -- 4.3.3 On the Mean of the Profiles -- 4.3.4 On the Variation of the Profiles -- 4.3.5 Mean and Variation of Profiles for the Temperature Data -- 4.4 Decomposing the Pearson Residual for Ordinal Variables -- 4.4.1 The Bivariate Moment Decomposition of γij−1 -- 4.4.2 BMD and the Temperature Data -- 4.5 Constructing a Low‐Dimensional Display -- 4.5.1 Standard Coordinates -- 4.5.2 Principal Coordinates -- 4.5.3 Practicalities of the Ordered Principal Coordinates -- 4.6 The Biplot Display -- 4.6.1 Definition -- 4.6.2 Ordered Column Isometric Biplot -- 4.6.3 Ordered Row Isometric Biplot -- 4.6.4 Ordered Isometric Biplots for the Temperature Data -- 4.7 Final Comments -- Chapter 5 Ordered Non‐symmetrical Correspondence Analysis -- 5.1 Introduction -- 5.2 The Goodman-Kruskal tau Index Revisited -- 5.3 Decomposing πi|j for Ordinal and Nominal Variables -- 5.3.1 The Hybrid Decomposition of πi|j -- 5.3.2 Hybrid Decomposition and the Shoplifting Data -- 5.4 Constructing a Low‐Dimensional Display -- 5.4.1 Standard Coordinates -- 5.4.2 Principal Coordinates -- 5.5 The Biplot -- 5.5.1 An Overview -- 5.5.2 Column Isometric Biplot -- 5.5.3 Column Isometric Biplot of the Shoplifting Data -- 5.5.4 Row Isometric Biplot -- 5.5.5 Row Isometric Biplot of the Shoplifting Data -- 5.5.6 Distance Measures and the Row Isometric Biplots -- 5.6 Some Final Words -- Part III Analysis of Multiple Categorical Variables -- Chapter 6 Multiple Correspondence Analysis -- 6.1 Introduction -- 6.2 Crisp Coding and the Indicator Matrix -- 6.2.1 Crisp Coding.
6.2.2 The Indicator Matrix -- 6.2.3 Crisp Coding and the Alligator Data -- 6.2.4 Application of Multiple Correspondence Analysis using the Indicator Matrix -- 6.3 The Burt Matrix -- 6.4 Stacking -- 6.4.1 A Definition -- 6.4.2 Stacking and the Alligator Data - Lake(Size)× Food -- 6.4.3 Stacking and the Alligator Data - Food(Size)× Lake -- 6.5 Final Comments -- Chapter 7 Multi‐way Correspondence Analysis -- 7.1 An Introduction -- 7.2 Pearson's Residual γijk−1 and the Partition of X2 -- 7.2.1 The Pearson Residual -- 7.2.2 The Partition of X2 -- 7.2.3 Partition of X2 for the Alligator Data -- 7.3 Symmetric Multi‐way Correspondence Analysis -- 7.3.1 Tucker3 Decomposition of γijk−1 -- 7.3.2 T3D and the Analysis of Two Variables -- 7.3.3 On the Choice of the Number of Components -- 7.3.4 Tucker3 Decomposition of γijk−1 and the Alligator Data -- 7.4 Constructing a Low‐Dimensional Display -- 7.4.1 Principal Coordinates -- 7.4.2 The Interactive Biplot -- 7.4.3 Column‐Tube Interactive Biplot for the Alligator Data -- 7.4.4 Row Interactive Biplot for the Alligator Data -- 7.5 The Marcotorchino Residual πi|j,k and the Partition of τM -- 7.5.1 The Marcotrochino Residual -- 7.5.2 The Partition of τM -- 7.5.3 Partition of τM for the Alligator Data -- 7.6 Non‐symmetrical Multi‐way Correspondence Analysis -- 7.6.1 Tucker3 Decomposition of πi|j,k -- 7.6.2 Tucker3 Decomposition of πi|j,k and the Alligator Data -- 7.7 Constructing a Low‐Dimensional Display -- 7.7.1 On the Choice of Coordinates -- 7.7.2 Column-Tube Interactive Biplot for the Alligator Data -- 7.8 Final Comments -- References -- Author Index -- Subject Index -- EULA.
Record Nr. UNINA-9910676630003321
Beh Eric J.  
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
Autore Beh Eric J.
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Descrizione fisica 1 online resource (243 pages) : illustrations
Disciplina 519.537
Collana Wiley Series in Probability and Statistics
Soggetto topico Correspondence analysis (Statistics)
ISBN 1-119-04196-1
1-119-04448-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Data Visualisation -- 1.2 Correspondence Analysis in a "Nutshell" -- 1.3 Data Sets -- 1.3.1 Traditional European Food Data -- 1.3.2 Temperature Data -- 1.3.3 Shoplifting Data -- 1.3.4 Alligator Data -- 1.4 Symmetrical Versus Asymmetrical Association -- 1.5 Notation -- 1.5.1 The Two‐way Contingency Table -- 1.5.2 The Three‐way Contingency Table -- 1.6 Formal Test of Symmetrical Association -- 1.6.1 Test of Independence for Two‐way Contingency Tables -- 1.6.2 The Chi‐squared Statistic for a Two‐way Table -- 1.6.3 Analysis of the Traditional European Food Data -- 1.6.4 The Chi‐squared Statistic for a Three‐way Table -- 1.6.5 Analysis of the Alligator Data -- 1.7 Formal Test of Asymmetrical Association -- 1.7.1 Test of Predictability for Two‐way Contingency Tables -- 1.7.2 The Goodman-Kruskal tau Index -- 1.7.3 Analysis of the Traditional European Food Data -- 1.7.4 Test of Predictability for Three‐way Contingency Tables -- 1.7.5 Marcotorchino's Index -- 1.7.6 Analysis of the Alligator Data -- 1.7.7 The Gray-Williams Index and Delta Index -- 1.8 Correspondence Analysis and R -- 1.9 Overview of the Book -- Part I Classical Analysis of Two Categorical Variables -- Chapter 2 Simple Correspondence Analysis -- 2.1 Introduction -- 2.2 Reducing Multi‐dimensional Space -- 2.2.1 Profiles Cloud of Points -- 2.2.2 Profiles for the Traditional European Food Data -- 2.2.3 Weighted Centred Profiles -- 2.3 Measuring Symmetric Association -- 2.3.1 The Pearson Ratio -- 2.3.2 Analysis of the Traditional European Food Data -- 2.4 Decomposing the Pearson Residual for Nominal Variables -- 2.4.1 The Generalised SVD of γij−1 -- 2.4.2 SVD of the Pearson Ratio's -- 2.4.3 GSVD and the Traditional European Food Data -- 2.5 Constructing a Low‐Dimensional Display.
2.5.1 Standard Coordinates -- 2.5.2 Principal Coordinates -- 2.6 Practicalities of the Low‐Dimensional Plot -- 2.6.1 The Two‐Dimensional Correspondence Plot -- 2.6.2 What is NOT Being Shown in a Two‐Dimensional Correspondence Plot? -- 2.6.3 The Three‐Dimensional Correspondence Plot -- 2.7 The Biplot Display -- 2.7.1 Definition -- 2.7.2 Isometric Biplots of the Traditional European Food Data -- 2.7.3 What is NOT Being Shown in a Two‐Dimensional Biplot? -- 2.8 The Case for No Visual Display -- 2.9 Detecting Statistically Significant Points -- 2.9.1 Confidence Circles and Ellipses -- 2.9.2 Confidence Ellipses for the Traditional European Food Data -- 2.10 Approximate p‐values -- 2.10.1 The Hypothesis Test and its p‐value -- 2.10.2 P‐values and the Traditional European Food Data -- 2.11 Final Comments -- Chapter 3 Non‐Symmetrical Correspondence Analysis -- 3.1 Introduction -- 3.2 Quantifying Asymmetric Association -- 3.2.1 The Goodman-Kruskal tau Index -- 3.2.2 The τ Index and the Traditional European Food Data -- 3.2.3 Weighted Centred Column Profile -- 3.2.4 Profiles of the Traditional European Food Data -- 3.3 Decomposing πi|j for Nominal Variables -- 3.3.1 The Generalised SVD of πi|j -- 3.3.2 GSVD and the Traditional Food Data -- 3.4 Constructing a Low‐Dimensional Display -- 3.4.1 Standard Coordinates -- 3.4.2 Principal Coordinates -- 3.5 Practicalities of the Low‐Dimensional Plot -- 3.5.1 The Two‐Dimensional Correspondence Plot -- 3.5.2 The Three‐Dimensional Correspondence Plot -- 3.6 The Biplot Display -- 3.6.1 Definition -- 3.6.2 The Column Isometric Biplot for the Traditional Food Data -- 3.6.3 The Three‐Dimensional Biplot -- 3.7 Detecting Statistically Significant Points -- 3.7.1 Confidence Circles and Ellipses -- 3.7.2 Confidence Ellipses for the Traditional Food Data -- 3.8 Final Comments.
Part II Ordinal Analysis of Two Categorical Variables -- Chapter 4 Simple Ordinal Correspondence Analysis -- 4.1 Introduction -- 4.2 A Simple Correspondence Analysis of the Temperature Data -- 4.3 On the Mean and Variation of Profiles with Ordered Categories -- 4.3.1 Profiles of the Temperature Data -- 4.3.2 Defining Scores -- 4.3.3 On the Mean of the Profiles -- 4.3.4 On the Variation of the Profiles -- 4.3.5 Mean and Variation of Profiles for the Temperature Data -- 4.4 Decomposing the Pearson Residual for Ordinal Variables -- 4.4.1 The Bivariate Moment Decomposition of γij−1 -- 4.4.2 BMD and the Temperature Data -- 4.5 Constructing a Low‐Dimensional Display -- 4.5.1 Standard Coordinates -- 4.5.2 Principal Coordinates -- 4.5.3 Practicalities of the Ordered Principal Coordinates -- 4.6 The Biplot Display -- 4.6.1 Definition -- 4.6.2 Ordered Column Isometric Biplot -- 4.6.3 Ordered Row Isometric Biplot -- 4.6.4 Ordered Isometric Biplots for the Temperature Data -- 4.7 Final Comments -- Chapter 5 Ordered Non‐symmetrical Correspondence Analysis -- 5.1 Introduction -- 5.2 The Goodman-Kruskal tau Index Revisited -- 5.3 Decomposing πi|j for Ordinal and Nominal Variables -- 5.3.1 The Hybrid Decomposition of πi|j -- 5.3.2 Hybrid Decomposition and the Shoplifting Data -- 5.4 Constructing a Low‐Dimensional Display -- 5.4.1 Standard Coordinates -- 5.4.2 Principal Coordinates -- 5.5 The Biplot -- 5.5.1 An Overview -- 5.5.2 Column Isometric Biplot -- 5.5.3 Column Isometric Biplot of the Shoplifting Data -- 5.5.4 Row Isometric Biplot -- 5.5.5 Row Isometric Biplot of the Shoplifting Data -- 5.5.6 Distance Measures and the Row Isometric Biplots -- 5.6 Some Final Words -- Part III Analysis of Multiple Categorical Variables -- Chapter 6 Multiple Correspondence Analysis -- 6.1 Introduction -- 6.2 Crisp Coding and the Indicator Matrix -- 6.2.1 Crisp Coding.
6.2.2 The Indicator Matrix -- 6.2.3 Crisp Coding and the Alligator Data -- 6.2.4 Application of Multiple Correspondence Analysis using the Indicator Matrix -- 6.3 The Burt Matrix -- 6.4 Stacking -- 6.4.1 A Definition -- 6.4.2 Stacking and the Alligator Data - Lake(Size)× Food -- 6.4.3 Stacking and the Alligator Data - Food(Size)× Lake -- 6.5 Final Comments -- Chapter 7 Multi‐way Correspondence Analysis -- 7.1 An Introduction -- 7.2 Pearson's Residual γijk−1 and the Partition of X2 -- 7.2.1 The Pearson Residual -- 7.2.2 The Partition of X2 -- 7.2.3 Partition of X2 for the Alligator Data -- 7.3 Symmetric Multi‐way Correspondence Analysis -- 7.3.1 Tucker3 Decomposition of γijk−1 -- 7.3.2 T3D and the Analysis of Two Variables -- 7.3.3 On the Choice of the Number of Components -- 7.3.4 Tucker3 Decomposition of γijk−1 and the Alligator Data -- 7.4 Constructing a Low‐Dimensional Display -- 7.4.1 Principal Coordinates -- 7.4.2 The Interactive Biplot -- 7.4.3 Column‐Tube Interactive Biplot for the Alligator Data -- 7.4.4 Row Interactive Biplot for the Alligator Data -- 7.5 The Marcotorchino Residual πi|j,k and the Partition of τM -- 7.5.1 The Marcotrochino Residual -- 7.5.2 The Partition of τM -- 7.5.3 Partition of τM for the Alligator Data -- 7.6 Non‐symmetrical Multi‐way Correspondence Analysis -- 7.6.1 Tucker3 Decomposition of πi|j,k -- 7.6.2 Tucker3 Decomposition of πi|j,k and the Alligator Data -- 7.7 Constructing a Low‐Dimensional Display -- 7.7.1 On the Choice of Coordinates -- 7.7.2 Column-Tube Interactive Biplot for the Alligator Data -- 7.8 Final Comments -- References -- Author Index -- Subject Index -- EULA.
Record Nr. UNINA-9910807726803321
Beh Eric J.  
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Metrics that make a difference : how to analyze change and error / / Robert Gilmore Pontius, Jr
Metrics that make a difference : how to analyze change and error / / Robert Gilmore Pontius, Jr
Autore Pontius Robert Gilmore
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (130 pages)
Disciplina 519.537
Collana Advances in geographic information science
Soggetto topico Correlation (Statistics)
Error analysis (Mathematics)
Mathematical statistics
ISBN 9783030707651
9783030707644
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- About the Author -- Chapter 1: Binary Variable Versus Binary Variable -- 1.1 Text -- 1.2 Discussion Questions -- References -- Chapter 2: Binary Variable Versus Rank Variable -- 2.1 Text -- 2.2 Discussion Questions -- References -- Chapter 3: Application of the Total Operating Characteristic -- 3.1 Text -- 3.2 Discussion Questions -- References -- Chapter 4: Categorical Variable Versus Categorical Variable -- 4.1 Text -- 4.2 Discussion Questions -- References -- Chapter 5: Application to Categorical Error Assessment with Sampling -- 5.1 Text -- 5.2 Discussion Questions -- References -- Chapter 6: Multiple Spatial Resolutions for Categorical Variables -- 6.1 Text -- 6.2 Discussion Questions -- References -- Chapter 7: Application to Categorical Temporal Change -- 7.1 Text -- 7.2 Discussion Questions -- References -- Chapter 8: Interval Variable Versus Interval Variable -- 8.1 Text -- 8.2 Discussion Questions -- References -- Chapter 9: Application to Interval Temporal Change -- 9.1 Text -- 9.2 Discussion Questions -- Reference -- Chapter 10: Indices of Agreement -- 10.1 Text -- 10.2 Discussion Questions -- References -- Chapter 11: Vector Variable Versus Vector Variable -- 11.1 Text -- 11.2 Discussion Questions -- References -- Chapter 12: Commandments to Avoid Deadly Sins -- 12.1 Text -- 12.2 Discussion Questions -- References -- Glossary.
Record Nr. UNINA-9910556890403321
Pontius Robert Gilmore  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multiple correspondence analysis and related methods / edited by Michael Grenacre and Jorg Blasius
Multiple correspondence analysis and related methods / edited by Michael Grenacre and Jorg Blasius
Pubbl/distr/stampa London, [etc.] : Chapman & Hall, 2006
Descrizione fisica 581 p. ; 25 cm.
Disciplina 519.537(Analisi della correlazione)
Collana Statistics in the social and behavioral sciences series
Soggetto topico Analisi multivariata
Correlazione
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-990005550050203316
London, [etc.] : Chapman & Hall, 2006
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Multivariate models and dependence concepts / Harry Joe
Multivariate models and dependence concepts / Harry Joe
Autore Joe, Harry
Pubbl/distr/stampa London : Chapman & Hall, 1997
Descrizione fisica xviii, 399 p. : ill. ; 22 cm
Disciplina 519.537
Collana Monographs on statistics and applied probability ; 73
Soggetto topico Linear models (Statistics)
Multivariate analysis
ISBN 0412073315
Classificazione AMS 62E10
AMS 62H20
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991001162789707536
Joe, Harry  
London : Chapman & Hall, 1997
Materiale a stampa
Lo trovi qui: Univ. del Salento
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Odds ratios in the analysis of contingency tables / Tm̀as Rudas
Odds ratios in the analysis of contingency tables / Tm̀as Rudas
Autore Rudas, Tamàs
Pubbl/distr/stampa Thousand Oaks : Sage, 1997
Descrizione fisica vi, 78 p. ; 22 cm
Disciplina 519.537
Collana Quantitative applications in the social sciences
Soggetto non controllato Serie
Analisi dei dati categorizzati
Modelli statistici per dati particolari
Manuali generali di consultazione
ISBN 0-7619-0362-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-990002883580403321
Rudas, Tamàs  
Thousand Oaks : Sage, 1997
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Rank correlation methods / Maurice Kendall and Jean Dickinson Gibbons.
Rank correlation methods / Maurice Kendall and Jean Dickinson Gibbons.
Autore Kendall, Maurice <1907-1983>
Edizione [5. ed.]
Pubbl/distr/stampa London : E. Arnold, c 1990
Descrizione fisica VIII, 260 p. ; 23 cm
Disciplina 519.537
Collana A Charles Griffin title
Soggetto non controllato Analisi di correlazione
ISBN 0-85264-305-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNINA-990006887810403321
Kendall, Maurice <1907-1983>  
London : E. Arnold, c 1990
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Rank correlation methods / Maurice G. Kendall
Rank correlation methods / Maurice G. Kendall
Autore Kendall, Maurice <1907-1983>
Edizione [4th ed.]
Pubbl/distr/stampa London : Griffin, 1970
Descrizione fisica viii, 202 p. ; 23 cm
Disciplina 519.537
Soggetto non controllato Analisi della correlazione
Correlazione
ISBN 0-85264-199-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-990008258660403321
Kendall, Maurice <1907-1983>  
London : Griffin, 1970
Materiale a stampa
Lo trovi qui: Univ. Federico II
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