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Applied Multivariate Statistics with R 1st ed. 2015 [Kõva köide]

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  • Formaat: Hardback, 393 pages, kõrgus x laius: 235x155 mm, kaal: 8137 g, 10 Tables, black and white; 108 Illustrations, color; 13 Illustrations, black and white; XVI, 393 p. 121 illus., 108 illus. in color., 1 Hardback
  • Sari: Statistics for Biology and Health
  • Ilmumisaeg: 13-Aug-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319140922
  • ISBN-13: 9783319140926
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Applied Multivariate Statistics with R 1st ed. 2015Suurem pilt
  • Formaat: Hardback, 393 pages, kõrgus x laius: 235x155 mm, kaal: 8137 g, 10 Tables, black and white; 108 Illustrations, color; 13 Illustrations, black and white; XVI, 393 p. 121 illus., 108 illus. in color., 1 Hardback
  • Sari: Statistics for Biology and Health
  • Ilmumisaeg: 13-Aug-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319140922
  • ISBN-13: 9783319140926
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319140926.html
Märksõnad:

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience withR is not necessary.

Arvustused

This book is so clearly explained with R code throughout that it could be used as a self-learning text for an applied multivariate course and should be assigned as a selflearning adjunct assignment for a graduate level theoretical multivariate course. The real-word examples are page turners and ubiquitous use of color and fancy graphs easily explained make this usually dry topic an exciting one. (Donna Pauler Ankerst, Biometrics, Vol. 73 (1), March, 2017)

This book demonstrates the process and outcomes for a wide array of multivariate statistical applications using program R. The chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. This book is strongly recommended for graduate-level statistics practitioners. (Hemang B. Panchal, Doodys Book Reviews, December, 2015)

Preface ix
1 Introduction 1(16)
1.1 Goals of Multivariate Statistical Techniques
1(2)
1.2 Data Reduction or Structural Simplification
3(2)
1.3 Grouping and Classifying Observations
5(4)
1.4 Examination of Dependence Among Variables
9(1)
1.5 Describing Relationships Between Groups of Variables
10(1)
1.6 Hypothesis Formulation and Testing
10(2)
1.7 Multivariate Graphics and Distributions
12(1)
1.8 Why R?
13(1)
1.9 Additional Readings
14(3)
2 Elements of R 17(38)
2.1 Getting Started in R
18(6)
2.1.1 R as a Calculator
18(1)
2.1.2 Vectors in R
19(4)
2.1.3 Printing in R
23(1)
2.2 Simulation and Simple Statistics
24(3)
2.3 Handling Data Sets
27(5)
2.4 Basic Data Manipulation and Statistics
32(5)
2.5 Programming and Writing Functions in R
37(3)
2.6 A Larger Simulation
40(6)
2.7 Advanced Numerical Operations
46(1)
2.8 Housekeeping
47(2)
2.9 Exercises
49(6)
3 Graphical Displays 55(34)
3.1 Graphics in R
55(3)
3.2 Displays for Univariate Data
58(5)
3.3 Displays for Bivariate Data
63(8)
3.3.1 Plot Options, Colors, and Characters
66(1)
3.3.2 More Graphics for Bivariate Data
67(4)
3.4 Displays for Three-Dimensional Data
71(4)
3.5 Displays for Higher Dimensional Data
75(9)
3.5.1 Pairs, Bagplot, and Coplot
75(3)
3.5.2 Glyphs: Stars and Faces
78(4)
3.5.3 Parallel Coordinates
82(2)
3.6 Additional Reading
84(1)
3.7 Exercises
85(4)
4 Basic Linear Algebra 89(28)
4.1 Apples and Oranges
89(2)
4.2 Vectors
91(3)
4.3 Basic Matrix Arithmetic
94(2)
4.4 Matrix Operations in R
96(6)
4.5 Advanced Matrix Operations
102(11)
4.5.1 Determinants
102(2)
4.5.2 Matrix Inversion
104(2)
4.5.3 Eigenvalues and Eigenvectors
106(2)
4.5.4 Diagonalizable Matrices
108(1)
4.5.5 Generalized Inverses
109(2)
4.5.6 Matrix Square Root
111(2)
4.6 Exercises
113(4)
5 The Univariate Normal Distribution 117(34)
5.1 The Normal Density and Distribution Functions
117(5)
5.2 Relationship to Other Distributions
122(1)
5.3 Transformations to Normality
122(4)
5.4 Tests for Normality
126(5)
5.5 Inference on Univariate Normal Means
131(6)
5.6 Inference on Variances
137(2)
5.7 Maximum Likelihood Estimation, Part I
139(8)
5.8 Exercises
147(4)
6 Bivariate Normal Distribution 151(22)
6.1 The Bivariate Normal Density Function
152(4)
6.2 Properties of the Bivariate Normal Distribution
156(2)
6.3 Inference on Bivariate Normal Parameters
158(5)
6.4 Tests for Bivariate Normality
163(1)
6.5 Maximum Likelihood Estimation, Part II
163(7)
6.6 Exercises
170(3)
7 Multivariate Normal Distribution 173(34)
7.1 Multivariate Normal Density and Its Properties
174(2)
7.2 Inference on Multivariate Normal Means
176(2)
7.3 Example: Home Price Index
178(4)
7.4 Maximum Likelihood, Part III: Models for Means
182(5)
7.5 Inference on Multivariate Normal Variances
187(2)
7.6 Fitting Patterned Covariance Matrices
189(5)
7.7 Tests for Multivariate Normality
194(8)
7.8 Exercises
202(5)
8 Factor Methods 207(24)
8.1 Principal Components Analysis
208(2)
8.2 Example 1: Investment Allocations
210(4)
8.3 Example 2: Kuiper Belt Objects
214(3)
8.4 Example 3: Health Outcomes in US Hospitals
217(1)
8.5 Factor Analysis
218(5)
8.6 Exercises
223(8)
9 Multivariable Linear Regression 231(26)
9.1 Univariate Regression
232(6)
9.2 Multivariable Regression in R
238(5)
9.3 A Large Health Survey
243(7)
9.4 Exercises
250(7)
10 Discrimination and Classification 257(30)
10.1 An Introductory Example
257(4)
10.2 Multinomial Logistic Regression
261(4)
10.3 Linear Discriminant Analysis
265(8)
10.4 Support Vector Machine
273(5)
10.5 Regression Trees
278(5)
10.6 Exercises
283(4)
11 Clustering 287(28)
11.1 Hierarchical Clustering
287(8)
11.2 K-Means Clustering
295(6)
11.3 Diagnostics, Validation, and Other Methods
301(7)
11.4 Exercises
308(7)
12 Time Series Models 315(24)
12.1 Introductory Examples and Simple Analyses
315(7)
12.2 Autoregressive Models
322(11)
12.3 Spectral Decomposition
333(3)
12.4 Exercises
336(3)
13 Other Useful Methods 339(22)
13.1 Ranking from Paired Comparisons
339(3)
13.2 Canonical Correlations
342(6)
13.3 Methods for Extreme Order Statistics
348(6)
13.4 Big Data and Wide Data
354(2)
13.5 Exercises
356(5)
Appendix: Libraries Used 361(2)
Selected Solutions and Hints 363(12)
References 375(6)
About the author 381(2)
Index 383
Daniel Zelterman, PhD, is Professor in the Department of Biostatistics at Yale University. His research areas include computational statistics, models for discrete valued data, and the design of clinical trials in cancer studies. In his spare time he plays oboe and bassoon in amateur orchestral groups and has backpacked hundreds of miles of the Appalachian Trail.