Statistical Comprehensive Evaluation
Date: 2018-10-25 Views: 70

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