Mathematical Statistics -Data Science 1-

Numbering Code G-LAS12 80035 LB54
G-LAS12 80035 LB13
Year/Term 2021 ・ First semester
Number of Credits 2 Course Type Lecture
Target Year Graduate students Target Student For all majors
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 経済や社会の様々な要因が絡み合うグローバル問題を解明するためには、データに潜む真理を探し出す統計解析が必須である。そのために有用な解析手法、特に多変量分析と時系列分析の基本を理解し、それらの具体的な応用事例を学習する。
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.
Course Goals 統計解析の理論を理解した上で、学生各自が興味を持つ問題について統計ソフトRで解析できるようになる。
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 【第1回】イントロダクション :多変量分析と時系列分析
Introduction: Multivariate analysis and time series analysis
Concept of data science: data acquisition, analysis, and modeling
【第3回】統計ソフトウエア R
Statistical analysis software R
Statistical quantity and distribution
【第5回】討論①:問題設定とグループ化 (データ)
Discussion 1: Problem setting and grouping
Regression analysis 1: Case studies, what to learn
Estimation and test
【第8回】回帰分析②:単回帰, ANOVA
Regression analysis 2: Single-regression and ANOVA
Regression analysis 3: Multi-regression and multi-collinearity
Principal component analysis, correlation matrix, and the eigenvalue problem
【第11回】討論②:データ可視化 (グラフ,基本統計量)
Discussion 2: Data visualization
Time series analysis 1: Case studies, what to learn
Time series analysis 2: Stationarity, ARIMA model, and maximum-likelihood
Time series analysis 3: Vector Auto Regression model and Impulse Response
Discussion 3: Analysis Results
Evaluation Methods and Policy 平常点と最終回に提示するレポートにより評価する。
Course Requirements None
Study outside of Class (preparation and review) 討論の準備を授業外学習として行うこと。
Textbooks Textbooks/References 印刷資料を配布する。
References, etc. 随時必要に応じて文献を紹介する。