Based on the construction of the excellent course Multivariate Statistical Analysis in Hebei Province, this book is close to the reality of provincial universities, takes the students' applied analytical skills as the main training goal, and takes methods and cases as the guidance, and trains students in all-round applied analytical skills such as method learning, case analysis, data processing, result discussion, literature reading and thesis writing. It is an application-oriented teaching material for senior undergraduates or graduate students majoring in statistics and other related majors in provincial universities. Multivariate statistical analysis is an important branch of statistics, which is widely used in natural science, social science and other fields, and is a powerful tool to explore a pluralistic world. The course of Multivariate Statistical Analysis in Hebei University of Economics and Business is the main course of statistics major and a provincial-level excellent course in Hebei Province. In the process of constructing top-quality courses, we combine rich teaching, scientific research practice and a large number of vivid cases, close to the reality of provincial colleges, take students' applied analytical skills as the main training goal, and take methods and cases as the guidance to carry out multivariate statistical analysis methods. As a provincial college, we personally understand the importance of cultivating the ability of applied analysis to students' future development, and also feel the helplessness of the lack of textbooks for pure application majors in China. Therefore, while constructing provincial-level excellent courses, combining with scientific research and teaching experience, closely following the lifeline of students' training and employment in provincial universities, taking application as the main line, combining methods and software to better solve practical problems as the core, we compiled this textbook "Applied Multivariate Statistical Analysis". This book expounds the functions and principles of various multivariate statistical methods in plain language. In view of specific cases, through SPSS, a widely used statistical analysis software in China, the realization and application of the method on the computer are taught, and various operation options of the statistical software are introduced, and explanations of data processing results are provided as far as possible. Combining literature reading and paper writing, students' applied analysis skills are cultivated. This book covers the commonly used multivariate statistical analysis methods. It is an applied textbook and teaching reference book for senior undergraduates or postgraduates majoring in statistics and economy, management and biomedical statistics in provincial universities, and can also be used as a practical reference book for social statisticians and data analysts. In the process of writing this book, graduate students, Liu Yang, Li and Ju Cui have done a lot of basic work. Tsinghua University Publishing House has given great support to the compilation and publication of teaching materials, and editor Chen Ming has done a lot of organizational work for this book. Thank you here! Due to the limited level of the author, there are inevitably omissions and mistakes in the book. I hope readers can put forward their valuable opinions for further revision.

Li Chunlin 20 13 Shijiazhuang July Author: Party Yao Chuan Min Qian

Series Name: 2 1 Century Excellent Textbook on Economic Management. Management science and engineering series

Publishing House: Tsinghua University Publishing House

ISBN:9787302283560

Shelf time: 20 12-6- 18

Publication date: 2065438+May 2002

Format: 16

Page number: 186

Version: 1- 1

Category: economic management books

This paper systematically introduces the classical theories and methods in multivariate statistical analysis by using multivariate statistical analysis, focusing on parameter estimation and hypothesis testing, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis and canonical correlation analysis of multivariate normal population. Strive to introduce the theory and application of various multivariate statistical methods in a simple way with statistical thought as the main line and spss software as the tool; Based on a large number of practical problems, this paper introduces the basic concepts and methods of multivariate statistical analysis, which is very practical. In the introduction of basic principles and methods, try to avoid complicated theoretical proofs, and explain theoretical methods through a large number of easy-to-understand examples, which is interesting and theoretical, and the theoretical difficulty is from shallow to deep, suitable for readers of different levels.

Applied Multivariate Statistical Analysis organically combines the study of spss software with case analysis, which embodies the application of multivariate statistical analysis methods, and is equipped with multimedia teaching courseware. It can be used as a teaching material for senior undergraduates or graduate students majoring in economics, management and other related majors, and is also suitable for readers who study multivariate statistical analysis by themselves. At the same time, it can also be used as a multi-dimensional data analysis reference book for practical workers in the fields of market research and data analysis. Applied multivariate statistical analysis

1 Chapter Overview of Multivariate Statistical Analysis

1. 1 Introduction

1.2 application background of multivariate statistical analysis

Chapter 2 multivariate normal distribution and its parameter estimation

2. 1 Basic concepts

2.2 multivariate normal distribution

2.3 Parameter estimation of multivariate normal distribution

utilize

The third chapter tests the mean vector and covariance matrix of multivariate normal distribution.

3. Test of1mean vector

3.2 Test of covariance matrix

utilize

Chapter IV Cluster Analysis

4. 1 the concept of cluster analysis

4.2 Distance and similarity coefficient

4.3 Systematic clustering method

4.4 Dynamic clustering method

4.5 Example analysis

utilize

Chapter V Discriminant Analysis

5. 1 The concept of discriminant analysis

5.2 Distance discrimination method

5.3 Fisher discriminant method

5.4 Bayesian discriminant method

5.5 Step by step discrimination method

5.6 Example analysis

utilize

Chapter VI Principal Component Analysis

6. 1 Concept and basic idea of principal component analysis

6.2 Mathematical model and geometric solution of total principal component analysis

6.3 Principal component analysis of samples

6.4 Comprehensive evaluation of principal component analysis

6.5 Principal component regression analysis

6.6 Example analysis

utilize

Chapter VII Factor Analysis

7. The concept of1factor analysis

7.2 Mathematical model of factor analysis

7.3 Solution of Factor Load Matrix

7.4 factor rotation

7.5 Factor score

7.6 Correlation test between variables

7.7 Example analysis

utilize

Chapter VIII Correspondence Analysis

8. 1 Correspondence analysis method and its basic idea

8.2 The basic principle of correspondence analysis method

8.3 Example analysis

utilize

Chapter 9 Canonical Correlation Analysis

9. 1 Basic concepts and ideas of canonical correlation analysis

9.2 Overall canonical correlation analysis

9.3 Canonical correlation analysis of samples

9.4 Example analysis

utilize

Chapter 10 Application of spss in Multivariate Statistical Analysis

10. 1 SPSS overview.

Application of 10.2 SPSS in multivariate analysis of variance

Application of 10.3 SPSS in discriminant analysis

Application of 10.4 SPSS in cluster analysis

Application of 10.5 SPSS in factor analysis and principal component analysis

Application of 10.6 SPSS in correspondence analysis

Application of 10.7 SPSS in canonical correlation analysis

refer to