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Applied Multivariate Data Analysis / J.D. Jobson / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (1991)
Titre : Applied Multivariate Data Analysis : Regression and Experimental Design Type de document : document électronique Auteurs : J.D. Jobson ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 1991 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XXV, 622 p Présentation : online resource ISBN/ISSN/EAN : 978-1-4612-0955-3 Langues : Anglais (eng) Descripteurs : Échantillonnage Tags : Statistics Economics Statistics for Business Mathematical Finance Insurance Statistics for Life Sciences Medicine Health Sciences Résumé : An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications Note de contenu : 1 Introduction -- 1.1 Multivariate Data Analysis, Data Matrices and Measurement Scales -- 1.2 The Setting -- 1.3 Review of Statistical Inference for Univariate Distributions -- Exercises for Chapter 1 -- Questions for Chapter 1 -- 2 Univariate Data Analysis -- 2.1 Data Analysis for Univariate Samples -- 2.2 Characteristics of Sample Distributions -- 2.3 Outliers -- 2.4 Assessing Normality -- 2.5 Transformations -- Cited Literature for Chapter 2 -- Exercises for Chapter 2 -- Questions for Chapter 2 -- 3 Bivariate Analysis for Qualitative Random Variables -- 3.1 Joint Distributions -- 3.2 Statistical Inference for Bivariate Random Variables -- 3.3 The Simple Linear Regression Model -- 3.4 Regression and Correlation in a Multivariate Setting -- Cited Literature for Chapter 3 -- Exercises for Chapter 3 -- Questions for Chapter 3 -- 4 Multiple Linear Regression -- 4.1 The Multiple Linear Regression Model -- 4.2 Variable Selection -- 4.3 Multicollinearity and Biased Regression -- 4.4 Residuals, Influence, Outliers and Model Validation -- 4.5 Qualitative Explanatory Variables -- 4.6 Additional Topics in Linear Regression -- Cited Literature and Additional References for Chapter 4 -- Exercises for Chapter 4 -- Questions for Chapter 4 -- 5 Analysis of Variance and Experimental Design -- 5.1 One-Way Analysis of Variance -- 5.2 Two-Way Analysis of Variance -- 5.3 Analysis of Covariance -- 5.4 Some Three-Way Analysis of Variance Models -- 5.5 Some Basics of Experimental Design -- 5.6 Multifactor Factorials, Fractional Replication Confounding and Incomplete Blocks -- 5.7 Random Effects Models and Variance Components -- 5.8 Repeated Measures and Split Plots Designs -- Cited Literature for Chapter 5 -- Exercises for Chapter 5 -- Questions for Chapter 5 -- 1. Matrix Algebra -- 1.1 Matrices -- Matrix, Transpose of a Matrix, Row Vector and Column Vector, Square Matrix, Symmetric Matrix, Diagonal Elements, Trace of a Matrix, Null or Zero Matrix, Identity Matrix, Diagonal Matrix, Submatrix -- 1.2 Matrix Operations -- Equality of Matrices, Addition of Matrices, Additive Inverse, Scalar Multiplication of a Matrix, Product of Two Matrices, Multiplicative Inverse, Idempotent Matrix, Kronecker Product -- 1.3 Determinants and Rank -- Determinant, Nonsingular, Relation Between Inverse and Determinant, Rank of a Matrix -- 1.4 Quadratic Forms and Positive Definite Matrices -- Quadratic Form, Congruent Matrix, Positive Definite, Positive Semidefinite, Negative Definite, Non-negative Definite -- 1.5 Partitioned Matrices -- Product of Partitioned Matrices, Inverse of a Partitioned Matrix, Determinant of a Partitioned Matrix -- 1.6 Expectations of Random Matrices -- 1.7 Derivatives of Matrix Expressions -- 2. Linear Algebra -- 2.1 Geometric Representation for Vectors -- 2.2 Linear Dependence And Linear Transformations -- 2.3 Systems of Equations -- Solution Vector for a System of Equations, Homogeneous Equations ? Trivial and Nontrivial Solutions -- 2.4 Column Spaces, Projection Operators and Least Squares -- Column Space, Orthogonal Complement, Projection, Ordinary Least Squares Solution Vector, Idempotent Matrix ? Projection Operator -- 3. Eigenvalue Structure and Singular Value Decomposition -- 3.1 Eigenvalue Structure for Square Matrices -- Eigenvalues and Eigenvectors, Characteristic Polynomial, Characteristic Roots, Latent Roots, Eigenvalues, Eigenvalues and Eigenvectors for Real Symmetric Matrices and Some Properties, Spectral Decomposition, Matrix Approximation, Eigenvalues for Nonnegative Definite Matrices -- 3.2 Singular Value Decomposition -- Left and Right Singular Vectors, Complete Singular Value Decomposition, Generalized Singular Value Decomposition, Relationship to Spectral Decomposition and Eigenvalues -- Data Appendix for Volume I -- Data Set D1, Data Set D2, Data Set D3, Data Set D4, Data Set D5, Data Set D6, Data Set D7, Data Set D8, Data Set D -- Table D1 -- Table D2 -- Table D3 -- Table D4 -- Table D5 -- Table D6 -- Table D7 -- Table D8 -- Table D9 -- Table Appendix -- Table 1 The Cumulative Distribution Function for the Standard Normal -- Table 3 Critical Values for the Chi-Square Distribution -- Table 5 Critical Values for the Studentized Range Distribution -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115528 Applied Multivariate Data Analysis : Regression and Experimental Design [document électronique] / J.D. Jobson ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 1991 . - XXV, 622 p : online resource. - (Springer Texts in Statistics, ISSN 1431-875X) .
ISBN : 978-1-4612-0955-3
Langues : Anglais (eng)
Descripteurs : Échantillonnage Tags : Statistics Economics Statistics for Business Mathematical Finance Insurance Statistics for Life Sciences Medicine Health Sciences Résumé : An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications Note de contenu : 1 Introduction -- 1.1 Multivariate Data Analysis, Data Matrices and Measurement Scales -- 1.2 The Setting -- 1.3 Review of Statistical Inference for Univariate Distributions -- Exercises for Chapter 1 -- Questions for Chapter 1 -- 2 Univariate Data Analysis -- 2.1 Data Analysis for Univariate Samples -- 2.2 Characteristics of Sample Distributions -- 2.3 Outliers -- 2.4 Assessing Normality -- 2.5 Transformations -- Cited Literature for Chapter 2 -- Exercises for Chapter 2 -- Questions for Chapter 2 -- 3 Bivariate Analysis for Qualitative Random Variables -- 3.1 Joint Distributions -- 3.2 Statistical Inference for Bivariate Random Variables -- 3.3 The Simple Linear Regression Model -- 3.4 Regression and Correlation in a Multivariate Setting -- Cited Literature for Chapter 3 -- Exercises for Chapter 3 -- Questions for Chapter 3 -- 4 Multiple Linear Regression -- 4.1 The Multiple Linear Regression Model -- 4.2 Variable Selection -- 4.3 Multicollinearity and Biased Regression -- 4.4 Residuals, Influence, Outliers and Model Validation -- 4.5 Qualitative Explanatory Variables -- 4.6 Additional Topics in Linear Regression -- Cited Literature and Additional References for Chapter 4 -- Exercises for Chapter 4 -- Questions for Chapter 4 -- 5 Analysis of Variance and Experimental Design -- 5.1 One-Way Analysis of Variance -- 5.2 Two-Way Analysis of Variance -- 5.3 Analysis of Covariance -- 5.4 Some Three-Way Analysis of Variance Models -- 5.5 Some Basics of Experimental Design -- 5.6 Multifactor Factorials, Fractional Replication Confounding and Incomplete Blocks -- 5.7 Random Effects Models and Variance Components -- 5.8 Repeated Measures and Split Plots Designs -- Cited Literature for Chapter 5 -- Exercises for Chapter 5 -- Questions for Chapter 5 -- 1. Matrix Algebra -- 1.1 Matrices -- Matrix, Transpose of a Matrix, Row Vector and Column Vector, Square Matrix, Symmetric Matrix, Diagonal Elements, Trace of a Matrix, Null or Zero Matrix, Identity Matrix, Diagonal Matrix, Submatrix -- 1.2 Matrix Operations -- Equality of Matrices, Addition of Matrices, Additive Inverse, Scalar Multiplication of a Matrix, Product of Two Matrices, Multiplicative Inverse, Idempotent Matrix, Kronecker Product -- 1.3 Determinants and Rank -- Determinant, Nonsingular, Relation Between Inverse and Determinant, Rank of a Matrix -- 1.4 Quadratic Forms and Positive Definite Matrices -- Quadratic Form, Congruent Matrix, Positive Definite, Positive Semidefinite, Negative Definite, Non-negative Definite -- 1.5 Partitioned Matrices -- Product of Partitioned Matrices, Inverse of a Partitioned Matrix, Determinant of a Partitioned Matrix -- 1.6 Expectations of Random Matrices -- 1.7 Derivatives of Matrix Expressions -- 2. Linear Algebra -- 2.1 Geometric Representation for Vectors -- 2.2 Linear Dependence And Linear Transformations -- 2.3 Systems of Equations -- Solution Vector for a System of Equations, Homogeneous Equations ? Trivial and Nontrivial Solutions -- 2.4 Column Spaces, Projection Operators and Least Squares -- Column Space, Orthogonal Complement, Projection, Ordinary Least Squares Solution Vector, Idempotent Matrix ? Projection Operator -- 3. Eigenvalue Structure and Singular Value Decomposition -- 3.1 Eigenvalue Structure for Square Matrices -- Eigenvalues and Eigenvectors, Characteristic Polynomial, Characteristic Roots, Latent Roots, Eigenvalues, Eigenvalues and Eigenvectors for Real Symmetric Matrices and Some Properties, Spectral Decomposition, Matrix Approximation, Eigenvalues for Nonnegative Definite Matrices -- 3.2 Singular Value Decomposition -- Left and Right Singular Vectors, Complete Singular Value Decomposition, Generalized Singular Value Decomposition, Relationship to Spectral Decomposition and Eigenvalues -- Data Appendix for Volume I -- Data Set D1, Data Set D2, Data Set D3, Data Set D4, Data Set D5, Data Set D6, Data Set D7, Data Set D8, Data Set D -- Table D1 -- Table D2 -- Table D3 -- Table D4 -- Table D5 -- Table D6 -- Table D7 -- Table D8 -- Table D9 -- Table Appendix -- Table 1 The Cumulative Distribution Function for the Standard Normal -- Table 3 Critical Values for the Chi-Square Distribution -- Table 5 Critical Values for the Studentized Range Distribution -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115528 Applied multivariate data analysis. Vol. 1, Regression and experimental design / J.D. Jobson / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (1991)
Titre : Applied multivariate data analysis. Vol. 1, Regression and experimental design Type de document : texte imprimé Auteurs : J.D. Jobson Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 1991 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XXIV-621 p. Format : 24 cm Accompagnement : 1 disquette 3.5 pouces ISBN/ISSN/EAN : 3-540-97660-4 Langues originales : Anglais (eng) Descripteurs : Analyse des données , Plan d'expérience , Régression , Statistique exploratoire multidimensionnelle , Statistique multidimensionnelle Index. décimale : 218 Statistique prévisionnelle multivariée Note sur les bibliographies ou index : Notes bibliogr. Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=34797 Applied multivariate data analysis. Vol. 1, Regression and experimental design [texte imprimé] / J.D. Jobson . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 1991 . - XXIV-621 p. ; 24 cm + 1 disquette 3.5 pouces. - (Springer Texts in Statistics, ISSN 1431-875X) .
ISBN : 3-540-97660-4
Langues originales : Anglais (eng)
Descripteurs : Analyse des données , Plan d'expérience , Régression , Statistique exploratoire multidimensionnelle , Statistique multidimensionnelle Index. décimale : 218 Statistique prévisionnelle multivariée Note sur les bibliographies ou index : Notes bibliogr. Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=34797 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 20000973915 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979642 218 JOB Ouvrage ENSAE 2. Statistique Disponible 30000910253 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979643 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979641 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979640 218 JOB Ouvrage ENSAE 2. Statistique Disponible 30000917042 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20001439097 218 JOBS 01 / T.1 (sans disquette) Ouvrage Ensai 2. Statistique Disponible Applied multivariate data analysis. Vol. 2, Categorical and multivariate methods / J.D. Jobson / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (1992)
Titre : Applied multivariate data analysis. Vol. 2, Categorical and multivariate methods Type de document : texte imprimé Auteurs : J.D. Jobson Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 1992 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XXVI-731 p. Format : 24 cm Accompagnement : disquette 5,25 pouces ISBN/ISSN/EAN : 3-540-97804-6 Langues originales : Anglais (eng) Descripteurs : Analyse des données , Distribution multidimensionnelle , Statistique exploratoire multidimensionnelle , Statistique multidimensionnelle Index. décimale : 218 Statistique prévisionnelle multivariée Note sur les bibliographies ou index : Notes bibliogr. Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=52773 Applied multivariate data analysis. Vol. 2, Categorical and multivariate methods [texte imprimé] / J.D. Jobson . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 1992 . - XXVI-731 p. ; 24 cm + disquette 5,25 pouces. - (Springer Texts in Statistics, ISSN 1431-875X) .
ISBN : 3-540-97804-6
Langues originales : Anglais (eng)
Descripteurs : Analyse des données , Distribution multidimensionnelle , Statistique exploratoire multidimensionnelle , Statistique multidimensionnelle Index. décimale : 218 Statistique prévisionnelle multivariée Note sur les bibliographies ou index : Notes bibliogr. Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=52773 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 30000910255 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979673 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979676 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979674 218 JOB Ouvrage ENSAE 2. Statistique Disponible 30000917043 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000973918 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20000979675 218 JOB Ouvrage ENSAE 2. Statistique Disponible 20001439095 218 JOBS 01 / T.2 (sans disquette) Ouvrage Ensai 2. Statistique Disponible Applied Multivariate Data Analysis / J.D. Jobson / Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer (1992)
Titre : Applied Multivariate Data Analysis : Volume II: Categorical and Multivariate Methods Type de document : document électronique Auteurs : J.D. Jobson ; SpringerLink (Online service) Editeur : Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer Année de publication : 1992 Collection : Springer Texts in Statistics, ISSN 1431-875X Importance : XXIX, 732 p Présentation : online resource ISBN/ISSN/EAN : 978-1-4612-0921-8 Langues : Anglais (eng) Descripteurs : Échantillonnage Tags : Statistics Economics Statistics for Business Mathematical Finance Insurance Statistics for Life Sciences Medicine Health Sciences Résumé : A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta Note de contenu : 6 Contingency Tables -- 6.1 Multivariate Data Analysis Data Matrices and Measurement Scales -- 6.2 Two-Dimensional Contingency Tables -- 6.3 Multidimensional Contingency Tables -- 6.4 The Weighted Least Squares Approach -- Cited Literature and References -- Exercises for Chapter 6 -- Questions for Chapter 6 -- 7 Multivariate Distributions Inference Regression and Canonical Correlation -- 7.1 Multivariate Random Variables and Samples -- 7.2 The Multivariate Normal Distribution -- 7.3 Testing for Normality Outliers and Robust Estimation -- 7.4 Inference for the Multivariate Normal -- 7.5 Multivariate Regression and Canonical Correlation -- Cited Literature and References -- Exercises for Chapter 7 -- Questions for Chapter 7 -- 8 Manova Discriminant Analysis and Qualitative Response Models -- 8.1 Multivariate Analysis of Variance -- 8.2 Discriminant Analysis -- 8.3 Qualitative Response Regression Models and Logistic Regression -- 9 Principal Components Factors and Correspondence Analysis -- 9.1 Principal Components -- 9.2 The Exploratory Factor Analysis Model -- 9.3 Singular Value Decomposition and Matrix Approximation -- 9.4 Correspondence Analysis -- Cited Literature and References -- Exercises for Chapter 9 -- Questions for Chapter 9 -- 10 Cluster Analysis and Multidimensional Scaling -- 10.1 Proximity Matrices Derived from Data Matrices -- 10.2 Cluster Analysis -- 10.3 Multidimensional Scaling -- Cited Literature and References -- Exercises for Chapter 10 -- Questions for Chapter 10 -- 1. Matrix Algebra -- 1.1 Matrices -- Matrix -- Transpose of a Matrix -- Row Vector and Column Vector -- Square Matrix -- Symmetric Matrix -- Diagonal Elements -- Trace of a Matrix -- Null or Zero Matrix -- Identity Matrix -- Diagonal Matrix -- Submatrix -- 1.2 Matrix Operations -- Equality of Matrices -- Addition of Matrices -- Additive Inverse -- Scalar Multiplication of a Matrix -- Product of Two Matrices -- Multiplicative Inverse -- Idempotent Matrix -- Kronecker Product -- 1.3 Determinants and Rank -- Determinant -- Nonsingular -- Relation Between Inverse -- and Determinant -- Rank of a Matrix -- 1.4 Quadratic Forms and Positive Definite Matrices -- Quadratic Form -- Congruent Matrix -- Positive Definite -- Positive Semidefinite -- Negative Definite -- Non-negative Definite -- 1.5 Partitioned Matrices -- Product of Partitioned Matrices -- Inverse of a Parti-tioned Matrix -- Determinant of a Partitioned Matrix -- 1.6 Expectations of Random Matrices -- 1.7 Derivatives of Matrix Expressions -- 2. Linear Algebra -- 2.1 Geometric Representation for Vectors -- n Dimensional Space -- Directed Line Segment -- Coordinates -- Addition of Vectors -- Scalar Multiplication -- Length of a Vector -- Angle Between Vectors -- Orthogonal Vectors -- Projection -- 2.2 Linear Dependence And Linear Transformations -- Linearly Dependent Vectors -- Linearly Independent Vectors -- Basis for an n-Dimensional Space -- Generation of a Vector Space and Rank of a Matrix -- Linear Transformation -- Orthogonal Transformation -- Rotation -- Orthogonal Matri -- 2.3 Systems of Equations -- Solution Vector for a System of Equations -- Homoge-neous Equations ? Trivial and Nontrivial Solutions -- 2.4 Column Spaces -- Projection Operators and Least -- Squares -- Column Space -- Orthogonal Complement -- Projection -- Ordinary Least Squares Solution Vector -- Idempotent Matrix ? Projection Operator -- 3. Eigenvalue Structure and Singular Value Decomposition -- 3.1 Eigenvalue Structure for Square Matrices -- Eigenvalues and Eigenvectors -- Characteristic Polynomial -- Characteristic Roots -- Latent Roots -- Eigen-values -- Eigenvalues and Eignevectors for Real Symmetric Matrices and Some Properties -- Spectral Decomposition -- Matrix Approximation -- Eigenvalues for Nonnegative Definite Matrices -- 3.2 Singular Value Decomposition -- Left and Right Singular Vectors -- Complete Singular Value Decomposition -- Generalized Singular Value Decomposition -- Relationship to Spectral Decomposition and Eigenvalues -- Data Appendix For Volume II -- Data Set V1 -- Data Set V2 -- Data Set V3 -- Data Set V4 -- Data Set V5 -- Data Set V6 -- Data Set V7 -- Data Set V8 -- Data Set V9 -- Data Set V10 -- Data Set Vll -- Data Set V12 -- Data Set V13 -- Data Set V14 -- Data Set V15 -- Data Set V16 -- Data Set V17 -- Data Set V18 -- Data Set V19 -- Data Set V20 -- Data Set V21 -- Data Set V22 -- Table V1 -- Table V2 -- Table V3 -- Table V4 -- Table V5 -- Table V6 -- Table V7 -- Table V8 -- Table V9 -- Table V10 -- Table V11 -- Table V12 -- Table V13 -- Table V14 -- Table V15 -- Table V16 -- Table V17 -- Table V18 -- Table V19 -- Table V20 -- Table V21 -- Table V22 -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115526 Applied Multivariate Data Analysis : Volume II: Categorical and Multivariate Methods [document électronique] / J.D. Jobson ; SpringerLink (Online service) . - Berlin ; Heidelberg (DEU) ; New York ; Bâle (CHE) : Springer, 1992 . - XXIX, 732 p : online resource. - (Springer Texts in Statistics, ISSN 1431-875X) .
ISBN : 978-1-4612-0921-8
Langues : Anglais (eng)
Descripteurs : Échantillonnage Tags : Statistics Economics Statistics for Business Mathematical Finance Insurance Statistics for Life Sciences Medicine Health Sciences Résumé : A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta Note de contenu : 6 Contingency Tables -- 6.1 Multivariate Data Analysis Data Matrices and Measurement Scales -- 6.2 Two-Dimensional Contingency Tables -- 6.3 Multidimensional Contingency Tables -- 6.4 The Weighted Least Squares Approach -- Cited Literature and References -- Exercises for Chapter 6 -- Questions for Chapter 6 -- 7 Multivariate Distributions Inference Regression and Canonical Correlation -- 7.1 Multivariate Random Variables and Samples -- 7.2 The Multivariate Normal Distribution -- 7.3 Testing for Normality Outliers and Robust Estimation -- 7.4 Inference for the Multivariate Normal -- 7.5 Multivariate Regression and Canonical Correlation -- Cited Literature and References -- Exercises for Chapter 7 -- Questions for Chapter 7 -- 8 Manova Discriminant Analysis and Qualitative Response Models -- 8.1 Multivariate Analysis of Variance -- 8.2 Discriminant Analysis -- 8.3 Qualitative Response Regression Models and Logistic Regression -- 9 Principal Components Factors and Correspondence Analysis -- 9.1 Principal Components -- 9.2 The Exploratory Factor Analysis Model -- 9.3 Singular Value Decomposition and Matrix Approximation -- 9.4 Correspondence Analysis -- Cited Literature and References -- Exercises for Chapter 9 -- Questions for Chapter 9 -- 10 Cluster Analysis and Multidimensional Scaling -- 10.1 Proximity Matrices Derived from Data Matrices -- 10.2 Cluster Analysis -- 10.3 Multidimensional Scaling -- Cited Literature and References -- Exercises for Chapter 10 -- Questions for Chapter 10 -- 1. Matrix Algebra -- 1.1 Matrices -- Matrix -- Transpose of a Matrix -- Row Vector and Column Vector -- Square Matrix -- Symmetric Matrix -- Diagonal Elements -- Trace of a Matrix -- Null or Zero Matrix -- Identity Matrix -- Diagonal Matrix -- Submatrix -- 1.2 Matrix Operations -- Equality of Matrices -- Addition of Matrices -- Additive Inverse -- Scalar Multiplication of a Matrix -- Product of Two Matrices -- Multiplicative Inverse -- Idempotent Matrix -- Kronecker Product -- 1.3 Determinants and Rank -- Determinant -- Nonsingular -- Relation Between Inverse -- and Determinant -- Rank of a Matrix -- 1.4 Quadratic Forms and Positive Definite Matrices -- Quadratic Form -- Congruent Matrix -- Positive Definite -- Positive Semidefinite -- Negative Definite -- Non-negative Definite -- 1.5 Partitioned Matrices -- Product of Partitioned Matrices -- Inverse of a Parti-tioned Matrix -- Determinant of a Partitioned Matrix -- 1.6 Expectations of Random Matrices -- 1.7 Derivatives of Matrix Expressions -- 2. Linear Algebra -- 2.1 Geometric Representation for Vectors -- n Dimensional Space -- Directed Line Segment -- Coordinates -- Addition of Vectors -- Scalar Multiplication -- Length of a Vector -- Angle Between Vectors -- Orthogonal Vectors -- Projection -- 2.2 Linear Dependence And Linear Transformations -- Linearly Dependent Vectors -- Linearly Independent Vectors -- Basis for an n-Dimensional Space -- Generation of a Vector Space and Rank of a Matrix -- Linear Transformation -- Orthogonal Transformation -- Rotation -- Orthogonal Matri -- 2.3 Systems of Equations -- Solution Vector for a System of Equations -- Homoge-neous Equations ? Trivial and Nontrivial Solutions -- 2.4 Column Spaces -- Projection Operators and Least -- Squares -- Column Space -- Orthogonal Complement -- Projection -- Ordinary Least Squares Solution Vector -- Idempotent Matrix ? Projection Operator -- 3. Eigenvalue Structure and Singular Value Decomposition -- 3.1 Eigenvalue Structure for Square Matrices -- Eigenvalues and Eigenvectors -- Characteristic Polynomial -- Characteristic Roots -- Latent Roots -- Eigen-values -- Eigenvalues and Eignevectors for Real Symmetric Matrices and Some Properties -- Spectral Decomposition -- Matrix Approximation -- Eigenvalues for Nonnegative Definite Matrices -- 3.2 Singular Value Decomposition -- Left and Right Singular Vectors -- Complete Singular Value Decomposition -- Generalized Singular Value Decomposition -- Relationship to Spectral Decomposition and Eigenvalues -- Data Appendix For Volume II -- Data Set V1 -- Data Set V2 -- Data Set V3 -- Data Set V4 -- Data Set V5 -- Data Set V6 -- Data Set V7 -- Data Set V8 -- Data Set V9 -- Data Set V10 -- Data Set Vll -- Data Set V12 -- Data Set V13 -- Data Set V14 -- Data Set V15 -- Data Set V16 -- Data Set V17 -- Data Set V18 -- Data Set V19 -- Data Set V20 -- Data Set V21 -- Data Set V22 -- Table V1 -- Table V2 -- Table V3 -- Table V4 -- Table V5 -- Table V6 -- Table V7 -- Table V8 -- Table V9 -- Table V10 -- Table V11 -- Table V12 -- Table V13 -- Table V14 -- Table V15 -- Table V16 -- Table V17 -- Table V18 -- Table V19 -- Table V20 -- Table V21 -- Table V22 -- Author Index Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=115526