Gaogang 1824
Outline of Jiangsu Higher Education Self-study Examination
30447 Data, Models and Decisions
Editor of Nanjing University (20 19)
Jiangsu province higher education self-study exam Committee office
I. Nature, Purpose and Requirements of the Course
First, the nature, model and decision-making of curriculum data
With the improvement of social informatization level and the general enhancement of scientific management consciousness, people show more and more interest in how to understand from the perspective of data. The data itself is meaningless, and the key is to use appropriate methods to analyze and process it. Only in this way can we explore the internal laws of the development and change of objective phenomena and better serve the needs of management decision-making.
Data, Model and Decision-making is a quantitative course, which focuses on the collection, description, analysis and interpretation of data, as well as the knowledge of management decision-making methods and technologies. Management decision-making is divided into two categories, one is rational decision-making and the other is behavioral decision-making. In the data analysis decision-making model, whether it is statistical decision-making characterized by uncertainty, scientific optimization decision-making characterized by certainty or game decision-making characterized by strategic interaction, it can be classified as rational decision-making. Since it is rational decision-making, it is necessary to establish some decision-making criteria, and then choose the decision-making scheme by measuring under the established criteria. On the one hand, this process needs to deal with research problems in a structured way, on the other hand, it also needs corresponding data. The former is to establish decision-making model, and the latter is to help realize calculation. From this point of view, the significance of data and models in decision analysis is self-evident. Data and models not only serve for decision analysis, but also have a close relationship. From the perspective of application, statistical methods emphasize empirical practice. Statistical analysis and decision-making do not have a large number of objective and accurate data, and statistical decision-making analysis can only stay in a purely theoretical state and cannot form specific analysis conclusions. In management and operation optimization and game decision analysis, although there is no need to have enough data like statistical analysis, the necessary numerical data of uncontrollable factors, such as relevant parameters in the model, must be determined in advance. Although enterprises have generally accumulated a large amount of data available for development and utilization, for one reason or another, the data itself will always be unsystematic, insufficient and incomplete. Therefore, the background data must be scientifically edited, processed, summarized and refined before it can be used for decision analysis. In this regard, this model plays an important role in the transformation. Through modeling, we can not only transform the value structure of data, but also deeply analyze and make decisions. Just like the production process, data is like "raw materials" and models are like "machines". The data raw materials are put into the model machine and processed by the model machine, and finally the output result "product" can be obtained, that is, management analysis and decision-making scheme.
At present, it has been widely developed and applied and plays an important role in all aspects of social and economic management. Taking enterprise management as an example, data and model tools will be used in production and operation, inventory management, quality control, resource utilization, site selection, product development and design, equipment maintenance and update, personnel arrangement, project planning, organization setting, information processing, investment portfolio, financing scheme, marketing, business situation prediction and competitive pricing. In the fields of social management, public service and data processing, it is precisely because of this that quantitative methods are advocated to be incorporated into the education system of economic management specialty in China's higher education system. Management is the eternal theme of human society, and it is always necessary and indispensable at any time and at any stage of development. With the continuous development of social economy, the management problems people encounter may be more complicated. Only by understanding scientific management methods can we make reasonable plans and action plans. The introduction of quantitative analysis method into the teaching system of economic management specialty reflects the high standard requirements of society for talent training. The future managers of enterprises and society should study hard the scientific methods of management and consciously use scientific methods to solve management problems in future practical work.
II. Introduction to the evaluation content of "Data, Model and Decision"
Data, models and decision-making are rich, and some methods may be difficult for students majoring in economic management. Therefore, in specific teaching, we can combine the principle and application conditions of this method with case analysis and the use of computer software.
Generally speaking, you need to learn the following by yourself:
The first chapter is an overview. Understand the significance of quantitative analysis in professional learning, understand the relationship between data, model and decision-making, pay attention to the principles and requirements of quantitative analysis, and master the general procedures of quantitative decision-making analysis.
Chapter II Source, Classification and Transformation of Data. Through the study of this chapter, we can systematically understand and master the basic methods of data collection, know the general types of management decision-making data, be familiar with the influencing factors of data quality and general inspection methods, and master the simple transformation and processing of data.
Chapter III Description and Analysis of Statistical Data. Familiar with the functions and production methods of various charts, master the calculation method and application precautions of data characteristic graphics, especially the comprehensive application of various characteristic graphics. It is best to use statistical description methods to carry out applied research on specific problems.
Chapter IV Parameter Sample Inference. Understand the concept of statistics, learn to use EXCEL to calculate the probability of "three inferred distributions", master the sampling distribution of commonly used statistics, and skillfully use EXCEL to realize parameter estimation and hypothesis testing.
Chapter V Analysis of Variance and Its Application. Understand the basic terms in variance analysis, be familiar with the basic ideas of variance analysis, master the use process of EXCEL variance analysis, and correctly interpret the results.
Chapter VI Regression model and its application. Regression model is an important part of statistical analysis. The study of this chapter needs to master the general form of linear regression model and its reflection function, and master the recognition of EXCEL results. Learn the linearization transformation of nonlinear regression model.
Chapter VII Time Series Analysis and Dynamic Prediction. Requirements of this chapter: Understand the basic requirements of time series function and compilation, master dynamic comparative analysis methods, and learn to measure and analyze long-term trends, seasonal changes and periodic changes.
Chapter VIII Risk Decision Analysis. To study this chapter, we should understand the basic elements of statistical decision-making, master the basic methods of uncertain decision-making, and be familiar with the basic principles and applications of risk decision-making.
Chapter IX Statistical Quality Management. Understand the influencing factors of quality change, be familiar with the general methods of quality analysis, master the making principle and identification method of control chart, and master the calculation and reading of process capability index.
Chapter 10 Principle and application of linear programming. Understand the basic composition and establishment process of linear programming model, learn the graphic method of two-dimensional linear programming model, and master some basic concepts and identification rules of optimal solution in linear programming.
The second chapter is the XI generalization of linear programming. In the study of this chapter, we need to master the judgment rules of dual solutions, be familiar with the sensitivity analysis results of spreadsheets, understand the common models of linear integer programming, and learn to solve linear integer programming and transportation problems with point-edge tables.
Chapter XII Simulation of Inventory Management and Control. Understand the basic terms of inventory management, master ABC analysis and decision-making methods, master the basic model of deterministic inventory and its application, and be familiar with simple random inventory decision-making methods.
Chapter 13 Queuing principle and application. Understand the basic terms of queuing system and learn the basic queuing model.
Chapter 14 Basic principles of the game. Master the basic elements of game analysis, and understand the basic concepts, solutions and related applications of two-person finite zero-sum and non-zero-sum games.
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