2017 Statistics and Data Analysis

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Academic unit or major
Undergraduate major in Transdisciplinary Science and Engineering
Kinouchi Tsuyoshi  Chiba Satoshi  Takahashi Fumitake  Iio Shunji  Hanaoka Shinya  Kawasaki Tomoya  Sagara Hiroshi 
Class Format
Lecture / Exercise     
Media-enhanced courses
Day/Period(Room No.)
Tue3-4(S514)  Fri3-4(S514)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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Course description and aims

Through lectures and exercises, this course is designed to teach methods of statistical analysis, estimation and testing that are required for processing and understanding the data obtained by experiments, measurements and simulations.

Student learning outcomes

By the end of this course, students will have acquired fundamental knowledge on statistics and data analysis, which includes methods for estimating probability density distribution, testing of statistical hypotheses, correlation analysis, regression analysis, multivariate analysis and time series analysis.


Probability, Statistics, Data

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Each lecture will include both teaching content and exercises, and exercises will be applied to confirm understanding of the lecture content. Structure will be: Review of previous lecture: 10 minutes; lecture: 60 minutes; exercise: 20 minutes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fundamentals of Probability Random Variable, Bayesian Probability
Class 2 Basics of Probability Distributions Probability Distribution, Normal Distribution
Class 3 Various Probability Distributions Binomial Distribution, Poisson Distribution
Class 4 Important theorem Law of Large Numbers, Central Limit Theorem
Class 5 Population and Sampling Population, Random Sampling
Class 6 Statistical Estimation Point Estimation, Interval Estimation
Class 7 Hypothesis Test Statistical Hypothesis
Class 8 Regression and Correlation Analysis Regression Analysis, Correlation Analysis
Class 9 Principal Component Analysis Principal Component Analysis
Class 10 Outline and Discussion of Group Work Outline and Discussion of Group Work
Class 11 Quantification Theory Quantification Theory I, II and II
Class 12 Other Multivariate Analysis Factor Analysis, Cluster Analysis
Class 13 Presentation of Group Work Presentation of Group Work
Class 14 Time Series Analysis Time Series Analysis, ARIMA
Class 15 Stochastic Process Stochastic Process, Markov Process


Alfred H-S. Ang and Wilson H. Tang (2007) Probability Concepts in Engineering, Emphasis on Application in Civil and Environmental Engineering, John Wiley & Sons. New York.

Reference books, course materials, etc.


Assessment criteria and methods

Exercise 45%, Group Work 15%, Final Examination 40%

Related courses

  • None

Prerequisites (i.e., required knowledge, skills, courses, etc.)


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