Course Name: Statistical Data Analysis

Course Code: S2293127

Semester: 3

Credit: 3

Program: Computer science，Electronics

Course Module: Specialized Compulsory

Responsible:Qingzhi Hou

E-mail: darcy.hou@gmail.com

Department: Tianjin International Engineering Institute

Time Allocation(1 credit hour = 45 minutes)

Course Description

This course is a required course designed for Engineering Master of Computer Science in TIEI. This course will systematically describe data file processing, the basic knowledge of probability and statistic, parameter estimation, hypothesis testing, variance analysis, regression analysis and so on. The purpose of this course is to equip students with the basic knowledge of data analysis, teach students how to apply scientific statistical theory and methods to understand objective things, enhance student’s ability to analyze and solve practical problems and lay the foundation for other courses.

Prerequisite

• Linear algebra: understanding determinant, matrix operations, vector space and so on.

• Higher mathematics: mastering function, limit, calculus and so on.

• Computer technology and applications: understanding basic computer technology and software applications.

Course Objectives

This course discuss probability and statistics to help students understand objective things and enhance student’s ability to analyze and solve practical problems. After this course, students should be able to:

• Master the basic knowledge of data analysis, probability, statistics and so on,

• Use computer software to solve practical problems of statistical data analysis problem, and to

• Lay a good foundation for learning other knowledge and curriculum.

Course Syllabus

• The data file processing: data collection and classification.

• The data processing: qualitative data analysis, quantitative data analysis and data correction analysis.

• Probability and statistics fundamentals: events and probability, random variables and probability distributions, normal distribution, population and sample.

• Parameter estimation: point estimation, moment estimation, unbiased, effectiveness and interval estimation.

• Hypothesis testing: basic ideas and the steps of hypothesis testing, the hypothesis testing of normal population distribution.

• Variance analysis: basic concepts, one-way variance analysis, multivariate variance analysis.

• Regression analysis: the concept of regression, analysis steps and so on.

• Use computer software to solve practical problems of statistical data analysis.

Textbooks & References

• R. Lyman Ott and Micheal T. Longnecker.An Introduction to Statistical Methods and Data Analysis. Duxbury Press, 2008.

• M. Meloun and J. Militky.Statistical Data Analysis: A Practical Guide. Woodhead Publishing Ltd, 2011.

• Elisa T. Lee and John Wenyu Wang.Statistical methods for survival data analysis. J. Wiley & Sons, 2003.

• John Keenan Taylor.Statistical techniques for data analysis. Lewis Publishers, 1990.

Capability Tasks

CT1: To understanding statistical data analysis, have analytical skills and the ability to synthesize knowledge.

CT2: To be able to use statistical data analysis related professional knowledge in the filed of science and technology: know and understand the basic concept and its connotation and relationship; grasp the concept of different methods learned, and its application.

CT3: To master statistical data analysis methods and tools: even not familiar with or without a full explanation, identify problems and use appropriate methods and tools to solve the problem, the use of computer tools, system analysis.

CS1: To master the basic theory of professional knowledge, and to understand the professional development status and trends.

CS2: To grasp comprehensively the core knowledge and related engineering technology knowledge of computer science, to be able to use core knowledge and engineering technology for system development.

Achievements

• To understand the data file processing, and to be familiar with data collection and classification. - Level: M

• To master the data processing, and to be familiar with qualitative data analysis, quantitative data analysis and data correction analysis. - Level: M

• To master probability and statistics fundamentals, and to be familiar with events and probability, random variables and probability distributions, normal distribution, population and sample. - Level: M

• To grasp parameter estimation, and to be familiar with point estimation, unbiased, effectiveness and interval estimation. - Level: M

• To be familiar with hypothesis testing, and to master the basic ideas and the steps of hypothesis testing, the hypothesis testing of normal population distribution. - Level: M

• To master variance analysis, and to be familiar with basic concepts, one-way variance analysis, multivariate variance analysis. - Level: M

• To grasp regression analysis, and to be familiar with the concept of regression, analysis steps and so on. - Level: M

• To be able to use computer software to solve practical problems of statistical data analysis. - Level: A

Students: Computer science,Year 1