An Introduction to Multivariate Data
A comprehensive overview of multivariate data and analysis
Providing an introduction to the subject of multivariate data analysis without delving deeply into underlying theory and concepts, An Introduction to Multivariate Data is an excellent resource for undergraduate statistics courses as well as for professionals who require an understanding of statistical techniques for analyzing their own data sets. Focused on real-world application, the book includes sample exercises in each chapter to firmly cement the concepts covered. Exercises range from simple problems that can be solved by hand or with a calculator, while others require the use of computer-based statistical software.
1 Introduction 1
2 Matrix Algebra 10
3 Basic Multivariate Statistics 22
4 Graphical Representation of Multivariate Data 41
5 Principal Components Analysis 49
6 Biplots 68
7 Correspondence Analysis 76
8 Cluster Analysis 87
9 Multidimensional Scaling 99
10 Linear Regression Analysis 118
11 Multivariate Analysis of Variance 131
12 Canonical Correlation Analysis 140
13 Discriminate Analysis and Canonical Variates Analysis 148
14 Loglinear Modelling 169
15 Factor Analysis 181
16 Other Latent Variable Models 195
17 Graphical Modelling 201
18 Data Mining 211
References 225
Index 227
2 Matrix Algebra 10
3 Basic Multivariate Statistics 22
4 Graphical Representation of Multivariate Data 41
5 Principal Components Analysis 49
6 Biplots 68
7 Correspondence Analysis 76
8 Cluster Analysis 87
9 Multidimensional Scaling 99
10 Linear Regression Analysis 118
11 Multivariate Analysis of Variance 131
12 Canonical Correlation Analysis 140
13 Discriminate Analysis and Canonical Variates Analysis 148
14 Loglinear Modelling 169
15 Factor Analysis 181
16 Other Latent Variable Models 195
17 Graphical Modelling 201
18 Data Mining 211
References 225
Index 227
Trevor Cox is a British academic and science communicator. He was a Senior Media fellow for EPSRC, and is a past-President of the Institute of Acoustics.
