Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods: Springer Texts in Statistics
Autor J.D. Jobsonen Limba Engleză Paperback – 23 oct 2012
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 393.02 lei 6-8 săpt. | |
| Springer – 27 sep 2012 | 393.02 lei 6-8 săpt. | |
| Springer – 23 oct 2012 | 637.84 lei 6-8 săpt. | |
| Hardback (2) | 400.43 lei 6-8 săpt. | |
| Springer – 3 sep 1991 | 400.43 lei 6-8 săpt. | |
| Springer – 25 iun 1992 | 644.28 lei 6-8 săpt. |
Din seria Springer Texts in Statistics
-
Preț: 421.81 lei - 18%
Preț: 868.56 lei - 18%
Preț: 714.35 lei -
Preț: 481.34 lei - 15%
Preț: 629.48 lei - 15%
Preț: 717.54 lei - 15%
Preț: 461.73 lei - 18%
Preț: 876.30 lei - 18%
Preț: 683.98 lei - 18%
Preț: 723.43 lei - 13%
Preț: 516.54 lei - 18%
Preț: 1082.81 lei - 15%
Preț: 650.73 lei -
Preț: 459.05 lei - 18%
Preț: 819.36 lei - 15%
Preț: 572.89 lei - 20%
Preț: 837.14 lei - 18%
Preț: 694.76 lei - 18%
Preț: 853.91 lei - 15%
Preț: 395.57 lei -
Preț: 283.86 lei - 18%
Preț: 911.49 lei - 15%
Preț: 625.75 lei - 18%
Preț: 717.69 lei -
Preț: 388.04 lei -
Preț: 388.40 lei -
Preț: 379.71 lei - 15%
Preț: 556.38 lei - 18%
Preț: 962.26 lei - 15%
Preț: 675.40 lei -
Preț: 391.54 lei - 18%
Preț: 861.13 lei - 15%
Preț: 577.64 lei - 23%
Preț: 741.10 lei - 19%
Preț: 587.70 lei - 15%
Preț: 630.77 lei - 15%
Preț: 656.52 lei -
Preț: 407.06 lei - 15%
Preț: 577.64 lei - 18%
Preț: 782.88 lei -
Preț: 387.47 lei -
Preț: 393.02 lei - 18%
Preț: 730.12 lei
Preț: 637.84 lei
Preț vechi: 750.40 lei
-15% Nou
Puncte Express: 957
Preț estimativ în valută:
112.89€ • 132.39$ • 98.98£
112.89€ • 132.39$ • 98.98£
Carte tipărită la comandă
Livrare economică 26 ianuarie-09 februarie 26
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781461269472
ISBN-10: 1461269474
Pagini: 768
Ilustrații: XXIX, 732 p.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 1.06 kg
Ediția:Softcover reprint of the original 1st ed. 1992
Editura: Springer
Colecția Springer
Seria Springer Texts in Statistics
Locul publicării:New York, NY, United States
ISBN-10: 1461269474
Pagini: 768
Ilustrații: XXIX, 732 p.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 1.06 kg
Ediția:Softcover reprint of the original 1st ed. 1992
Editura: Springer
Colecția Springer
Seria Springer Texts in Statistics
Locul publicării:New York, NY, United States
Public țintă
Professional/practitionerCuprins
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 SomeProperties.- 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.
Recenzii
"On the whole this volume on applied multivariate data analysis is a comprehensive treatise which will support students and teachers to a full extent in their coursework and researchers will find an easy ready-made material for the analysis of their multivariate data to arrive at correct conclusions. This is a masterpiece text." (Zentralblatt fuer Mathematik)
Caracteristici
Includes supplementary material: sn.pub/extras