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
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
Publication
Related Contributor
Related Location
Related Agents
Related Authorities
Related Subjects
Related Items
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
Dimensions
unknown
{'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

#### Library Locations

• Folsom Library
110 8th St, Troy, NY, 12180, US
42.729766 -73.682577