Basics of Matrix Algebra for Statistics with R

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A01=Nick Fieller
advanced statistical methods
Author_Nick Fieller
canonical correlation analysis
Canonical Variates
Category=PBF
Category=PBT
data modeling techniques
Distinct Eigenvalues
eigenanalysis
elementary matrix algebra
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Full Column Rank
Full Row Rank
Linear Discriminant Analysis
Linear Models
Linearly Independent
LRT Statistic
manipulation of matrices
Mass Library
Matrices in R
matrix algebra for statistical analysis in R
matrix calculus
Minimum Variance Unbiased Linear Estimator
Multivariate Analysis
multivariate statistics
Non-zero Eigenvalues
numerical calculations in R
Positive Semi-definite
principal component analysis
Product ABC
QR Decomposition
quantitative analysis
Real Symmetric Matrices
Row Rank
Sample Principal Components
Sample Variance Matrix
Saved Workspace
Schur Product
Singular Vectors
statistical computing
Structural Linear Dependencies
Union Intersection Tests
Variance Matrix
Vector and Matrix Calculus
Vector Eigen

Product details

  • ISBN 9780367783457
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 31 Mar 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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A Thorough Guide to Elementary Matrix Algebra and Implementation in R

Basics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses, such as multivariate data analysis and linear models. It also covers advanced topics, such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices, for those who want to delve deeper into the subject.

The book introduces the definition of a matrix and the basic rules of addition, subtraction, multiplication, and inversion. Later topics include determinants, calculation of eigenvectors and eigenvalues, and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques, including principal component analysis, canonical correlation analysis, and linear modeling.

In addition to the algebraic manipulation of matrices, the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers.

Avoiding vector spaces and other advanced mathematics, this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.

Dr. Nick Fieller is a retired senior lecturer in the School of Mathematics and Statistics and an honorary research fellow in archaeology at the University of Sheffield. His research interests include multivariate data analysis and statistical modeling in the pharmaceutical industry, archaeology, and forensic sciences.

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