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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
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- School of Science
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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Chemical Science and Engineering
- Instructor(s)
- Kubouchi Masatoshi
- Class Format
- Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(S422)
- Group
- -
- Course number
- CAP.E433
- Credits
- 1
- Academic year
- 2016
- Offered quarter
- 4Q
- Syllabus updated
- 2017/1/11
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course offers the basic methodology to analyze a statistical data from an objective viewpoint and a subjective viewpoint.
Student learning outcomes
By the end of this course, students will be able to understand:
1) How to handle objective data and to apply it.
2) How to handle subjective data.
Keywords
data, random variable, probability density function, sample, normal distribution, log-normal distribution, Weibull distribution, estimation, testing, Bayes' theorem, posterior probability
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Required learning should be completed outside of the classroom for preparation and review purpose.
Towards the end of class, students are given exercise or homework problems related to what is taught.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Basic of Stastics, probability and dandom variable Data & stastistic, probability, sample, independent event, complementafy event, conditional probability and random variable are lerned. | case study related on conditional probability |
| Class 2 | Probability Distribution (1) binomial distribution, Poisson distribution and nomal distribution are lerned. | applicatin exercises on normal distribution |
| Class 3 | Probability Distribution (2) log-nomal distribution and Weibull distribution are lerned. | applicaton exercises on Weibull distribution |
| Class 4 | Sample poluation and the sample methodorogy of sample survey is lerned. | exersice on sample survey |
| Class 5 | Estimation and Testing method of stastical estimation and tesiteing are lerned | case study related on estimation |
| Class 6 | Subjective statistics Bayes' theorem is lened | exersice on conditional probability based on Bayesian |
| Class 7 | Bayesian statistics (1) Baysian networks and updating of postenior probability are lerned. | case study related on updating of postenior probability |
| Class 8 | Bayesian statistics (2) Baysian estimation is lerned. | comparison of maximum-likelihood, MCMC and Baysian estimation |
Textbook(s)
Course materials are provided during class
Reference books, course materials, etc.
Course materials are provided during class
Assessment criteria and methods
Exercise problems (50%), Reports (50%)
Related courses
- Advanced statistical analysis II
Prerequisites (i.e., required knowledge, skills, courses, etc.)
N/A
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
mkubouch[at]chemeng.titech.ac.jp
