Mathematical Statistics-Data Science1-
| Numbering Code |
G-GAIS00 54005 LB54 G-GAIS00 54005 LB13 |
Year/Term | 2022 ・ First semester | |
|---|---|---|---|---|
| Number of Credits | 2 | Course Type | Lecture | |
| Target Year | From 1st to 3rd year students | Target Student | ||
| Language | Japanese and English | Day/Period | Tue.2 | |
| Instructor name | IKEDA YUICHI (Graduate School of Advanced Integrated Studies in Human Survivability Professor) | |||
| Outline and Purpose of the Course |
Statistical analysis to find the truth behind the data is indispensable to elucidate global problems involving various economic and social factors. This course aims to provide students with a basic understanding of useful analysis methods, especially multivariate analysis and time series analysis, and to study these methods' specific applications. This course is designed to provide students with an understanding of the fundamentals of statistical analysis, especially multivariate and time series analysis, and to learn specific applications of these techniques. In this course, students will learn about literacy and mining, which are the basics of data science. At the same time, we will strive to improve English proficiency in this field through English and Japanese lectures. |
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| Course Goals | With an understanding of statistical analysis theory, students will be able to use statistical software R to analyze problems of interest to each student. | |||
| Schedule and Contents |
【1st】Introduction: Multivariate analysis and time series analysis 【2nd】Concept of data science: data acquisition, analysis, and modeling 【3rd】Statistical analysis software R 【4th】Statistical quantity and distribution 【5th】Discussion 1: Problem setting and grouping 【6th】Regression analysis 1: Case studies, what to learn 【7th】Estimation and test 【8th】Regression analysis 2: Single-regression and ANOVA 【9th】Regression analysis 3: Multi-regression and multi-collinearity 【10th】Principal component analysis, correlation matrix, and the eigenvalue problem 【11th】Discussion 2: Data visualization 【12th】Time series analysis 1: Case studies, what to learn 【13th】Time series analysis 2: Stationarity, ARIMA model, and maximum-likelihood 【14th】Time series analysis 3: Vector Auto Regression model and Impulse Response 【15th】Discussion 3: Analysis Results |
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| Evaluation Methods and Policy | Evaluation is made based on behavior in class and the final report. | |||
| Course Requirements | None | |||
| Study outside of Class (preparation and review) | Preparation of discussion has to be made as assignment. | |||
| Textbooks | Textbooks/References | Printed materials will be distributed in class. | ||
| References, etc. | References will be shown according to needs. | |||
