Statistical Comprehensive Evaluation
Course No.: SMJ2221115 Credit(s): 2
Course Description
Statistical comprehensive evaluation is a statistical analysis method, which is based on the index system reflecting the overall social and economic phenomena, combined with various qualitative materials and building comprehensive evaluation model, to get the comprehensive evaluation value, and to make a clear assessment and excretion of the evaluated phenomenon. The commonly used methods of comprehensive evaluation are: comprehensive scoring, efficiency coefficient, and average index. As an elective for statistics, this course is designed to enable students to skillfully apply the course method, to write and publish academic papers.
Course Learning Outcomes
After studying the course, students grasp the concept of comprehensive evaluation, basic methods, and combine practical problems with comprehensive evaluation methods to carry out empirical analysis. Students are required to apply various methods to write academic papers independently.
Relationship to Other Courses
The prerequisites for this course are Probability Theory and Mathematical Statistics, Statistical Software.
Textbook and Reading Lists
Textbook:
Dong Du, Qinhua Pang, Modern Comprehensive Evaluation Method and Case Selection. Tsinghua University Press, September 2005.
Suggested reading lists:
Yonghong Hu, Sihui He, Comprehensive Evaluation Method. Science Press, 2000.
Weihua Su, Systematic Analysis of Multi Objective Comprehensive Evaluation Method. China Price Press, 2002
Dong Qiu, Systematic Analysis of Multi Objective Comprehensive Evaluation Method. China Statistics Press, 1991.
Course Assessment
Item | Title | Weighting (%) |
1 | Task in home | 10% |
2 | Test and Questions in class | 20% |
3 | Final Assignment | 70% |
Course Schedule
Week | Topics | Text |
1 | Introduction | Chapters 1 |
2 | An Overview of the Method of Index Selection | Chapters 2 |
3 | Dimensionless Method of Index | Chapters 3 |
4 | Index Weighting Method | Chapters 4 |
5 | Comprehensive Index Method | Chapters 5 |
6 | Analytic Hierarchy Process | Chapter 6 |
7 | Topsis | Chapter 7 |
8 | Grey Model GM | Chapter 8 |
9 | Fuzzy Mathematical Method | Chapter 9 |
10 | The Principle of Entropy Method | Chapter 10 |
11 | Neural Network | Chapter 11 |
12 | Factor Analysis | Chapter 12 |
13 | Data Envelopment Analysis | Chapter 13 |
14 | Principal Component Analysis | Chapter 14 |
15 | Combinatorial Prediction | Chapter 15 |
16-17 | Statistical Software | Lab |
18 | Review | |