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The Resource Applied Multivariate Statistics with R

Applied Multivariate Statistics with R

Label
Applied Multivariate Statistics with R
Title
Applied Multivariate Statistics with R
Creator
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Statistics for Biology and Health Ser
Applied Multivariate Statistics with R
Label
Applied Multivariate Statistics with R
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=3568013
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Acknowledgments -- Contents -- 1 Introduction -- 1.1 Goals of Multivariate Statistical Techniques -- 1.2 Data Reduction or Structural Simplification -- 1.3 Grouping and Classifying Observations -- 1.4 Examination of Dependence Among Variables -- 1.5 Describing Relationships Between Groups of Variables -- 1.6 Hypothesis Formulation and Testing -- 1.7 Multivariate Graphics and Distributions -- 1.8 Why R? -- 1.9 Additional Readings -- 2 Elements of R -- 2.1 Getting Started in R -- 2.1.1 R as a Calculator -- 2.1.2 Vectors in R -- 2.1.3 Printing in R -- 2.2 Simulation and Simple Statistics -- 2.3 Handling Data Sets -- 2.4 Basic Data Manipulation and Statistics -- 2.5 Programming and Writing Functions in R -- 2.6 A Larger Simulation -- 2.7 Advanced Numerical Operations -- 2.8 Housekeeping -- 2.9 Exercises -- 3 Graphical Displays -- 3.1 Graphics in R -- 3.2 Displays for Univariate Data -- 3.3 Displays for Bivariate Data -- 3.3.1 Plot Options, Colors, and Characters -- 3.3.2 More Graphics for Bivariate Data -- 3.4 Displays for Three-Dimensional Data -- 3.5 Displays for Higher Dimensional Data -- 3.5.1 Pairs, Bagplot, and Coplot -- 3.5.2 Glyphs: Stars and Faces -- 3.5.3 Parallel Coordinates -- 3.6 Additional Reading -- 3.7 Exercises -- 4 Basic Linear Algebra -- 4.1 Apples and Oranges -- 4.2 Vectors -- 4.3 Basic Matrix Arithmetic -- 4.4 Matrix Operations in R -- 4.5 Advanced Matrix Operations -- 4.5.1 Determinants -- 4.5.2 Matrix Inversion -- 4.5.3 Eigenvalues and Eigenvectors -- 4.5.4 Diagonalizable Matrices -- 4.5.5 Generalized Inverses -- 4.5.6 Matrix Square Root -- 4.6 Exercises -- 5 The Univariate Normal Distribution -- 5.1 The Normal Density and Distribution Functions -- 5.2 Relationship to Other Distributions -- 5.3 Transformations to Normality -- 5.4 Tests for Normality -- 5.5 Inference on Univariate Normal Means
  • 5.6 Inference on Variances -- 5.7 Maximum Likelihood Estimation, Part I -- 5.8 Exercises -- 6 Bivariate Normal Distribution -- 6.1 The Bivariate Normal Density Function -- 6.2 Properties of the Bivariate Normal Distribution -- 6.3 Inference on Bivariate Normal Parameters -- 6.4 Tests for Bivariate Normality -- 6.5 Maximum Likelihood Estimation, Part II -- 6.6 Exercises -- 7 Multivariate Normal Distribution -- 7.1 Multivariate Normal Density and Its Properties -- 7.2 Inference on Multivariate Normal Means -- 7.3 Example: Home Price Index -- 7.4 Maximum Likelihood, Part III: Models for Means -- 7.5 Inference on Multivariate Normal Variances -- 7.6 Fitting Patterned Covariance Matrices -- 7.7 Tests for Multivariate Normality -- 7.8 Exercises -- 8 Factor Methods -- 8.1 Principal Components Analysis -- 8.2 Example 1: Investment Allocations -- 8.3 Example 2: Kuiper Belt Objects -- 8.4 Example 3: Health Outcomes in US Hospitals -- 8.5 Factor Analysis -- 8.6 Exercises -- 9 Multivariable Linear Regression -- 9.1 Univariate Regression -- 9.2 Multivariable Regression in R -- 9.3 A Large Health Survey -- 9.4 Exercises -- 10 Discrimination and Classification -- 10.1 An Introductory Example -- 10.2 Multinomial Logistic Regression -- 10.3 Linear Discriminant Analysis -- 10.4 Support Vector Machine -- 10.5 Regression Trees -- 10.6 Exercises -- 11 Clustering -- 11.1 Hierarchical Clustering -- 11.2 K-Means Clustering -- 11.3 Diagnostics, Validation, and Other Methods -- 11.4 Exercises -- 12 Time Series Models -- 12.1 Introductory Examples and Simple Analyses -- 12.2 Autoregressive Models -- 12.3 Spectral Decomposition -- 12.4 Exercises -- 13 Other Useful Methods -- 13.1 Ranking from Paired Comparisons -- 13.2 Canonical Correlations -- 13.3 Methods for Extreme Order Statistics -- 13.4 Big Data and Wide Data -- 13.5 Exercises -- Appendix: Libraries Used
  • Selected Solutions and Hints -- References -- About the author -- Index
http://library.link/vocab/cover_art
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Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4383882'}
Extent
1 online resource (401 pages)
Form of item
online
Isbn
9783319140933
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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