In many fields of Industrial Engineering and Economics, we conduct research by collecting a wide variety of data, analyzing them, and validating hypotheses. The goal of this course is to learn various methods for collection and analysis of data necessary for industrial engineering and economics research.
Each lecture is followed by an exercise for comprehension of the methods learned.
By the end of this course, students are expected to:
(1) understand characteristics of various kinds of data.
(2) understand characteristics of various methods of data collection and be able to select appropriate methods depending on purposes.
(3) understand characteristics of various methods of data analysis and be able to select appropriate methods depending on purposes.
qualitative data, quantitative data, statistics, multivariable analysis
|✔ Specialist skills||Intercultural skills||Communication skills||✔ Critical thinking skills||✔ Practical and/or problem-solving skills|
A pair of lecture and exercise is a basic unit for this course. Knowledge and methods learned in lecture is further exercised in the following class.
|Course schedule||Required learning|
|Class 1||Introduction + data + interview||To understand goals, structure and grading of this course. To understand the concept and kinds of data. To understand methods of interview.|
|Class 2||Qualitative data analysis||Learn and experience actual methods for data analysis of qualitative data.|
|Class 3||Questionnaire||Learn and experience design of and investigation using questionnaires.|
|Class 4||Data analysis software||Install and setup statistical analysis software.|
|Class 5||Viewing data||Learn and experience methods for viewing overview of data through descriptive statistics, histograms, and plots.|
|Class 6||Comparison, correlation, and regression||Learn and experience methods for investigating multiple data, through t-tests, correlation analysis, and regression analysis.|
|Class 7||Factor analysis and discriminant analysis||Learn and experience factor analysis and discriminant analysis.|
|Class 8||Presentation||Present outcomes of individual data analysis.|
No textbook is set. Class materials are provided in the classes.
No special references are set. Necessary information is provided in class.
Students must have successfully completed both Statistics for Industrial Engineering and Economics (IEE.A205) and Industrial Engineering (IEE.C202) or have equivalent knowledge.
Students must bring own laptop to be used in exercise every week.