学部・大学院区分
Undergraduate / Graduate
開・博前
時間割コード
Registration Code
3051020
科目区分
Course Category
共通・基礎
Basic
科目名 【日本語】
Course Title
基礎統計学
科目名 【英語】
Course Title
Basic Statistics
コースナンバリングコード
Course Numbering Code
INT1L5102E
担当教員 【日本語】
Instructor
藤川 清史 ○ 梅村 哲夫
担当教員 【英語】
Instructor
FUJIKAWA Kiyoshi ○ UMEMURA Tetsuo
単位数
Credits
2
開講期・開講時間帯
Term / Day / Period
春集中 その他 その他
Intensive(Spring) Other Other
授業形態
Course style
講義
Lecture


授業の目的 【日本語】
Goals of the Course(JPN)
この講義は、数理統計学の入門コースです。 この講義の目的は、標本を用いて母集団のパラメータを推定・検定するという「数理統計学」の基本的な考え方を理解することです。言い換えれば、この講義で、受講生が誤差や有為水準の意味を理解することができます。
授業の目的 【英語】
Goals of the Course
This lecture is an introductory course in mathematical statistics. The purpose of this lecture is to understand the basic idea of "mathematical statistics", which is to estimate and test the parameters of a population using samples. In other words, this lecture will help students understand the meaning of errors and significant levels.
到達目標 【日本語】
Objectives of the Course(JPN)
受講生は、母平均・母比率の推定、母平均・母比率検定、グループ間の母平均・母比率の差の検定、および回帰分析の意味が直感的に理解できます。 受講生は、ExcelとE-viewsを用いて、母平均・母比率の推定、母平均・母比率検定、グループ間の母平均・母比率検定、および回帰分析の実習を行うので、それらの作業プロセスが身につきます。
到達目標 【英語】
Objectives of the Course
In this lecture, estimation of population mean / population ratio, test of population mean / population ratio, test of difference in population means / population ratios between groups, and regression analysis are explained, and students can intuitively understand their meanings. And the students can learn the procedures of estimation of population mean / population ratio, test of population mean / population ratio, test of difference in population means / population ratios between groups, and regression analysis through practices using Excel and E-views.
授業の内容や構成
Course Content / Plan
Part 1 Distribution
01 Introduction to mathematical statistics
02 Normal distribution
03 Sample distribution: T-distribution and Chi-square distribution
04 Sample distribution: Chi-square distribution and F-distribution

Part 2 Estimation and statistical test of parameters
05 Estimation of population mean, variance, and ratio 1
06 Estimation of population mean, variance, and ratio 2
07 Estimation of population mean, variance, and ratio 3
08 Test of population mean, variance, and ratio 1
09 Test of population mean, variance, and ratio 2
10 Test of population mean, variance, and ratio 3

Part 3 Other statistical test
11 T-Test for difference of population means and ratios of two groups
12 Chi square test for fitness and independence
13 Analysis of variance (ANOVA) for difference of means and ratios among groups
14 Correlation and regression analysis 1
15 Correlation and regression analysis 2 (Introduction to E-views)
履修条件・関連する科目
Course Prerequisites and Related Courses
There is no precondition to take this course. The participants of this course, however, are expected to have completed information processing course.
成績評価の方法と基準
Course Evaluation Method and Criteria
Performance in the class: 20% and term report baed on the lecture materials: 80%.
Credit is given if the socre is C- or higher for each graded criterion.
教科書・参考書
Textbook/Reference Book
Textbook: : No textbook is used in this lecture. Lecture materials are distributed through One-Drive.
Reference: David M. Levine, Kathryn A. Szabat, and David F. Stephan (2015), Business Statistics: A First Course, 7th Edition, Pearson. ISBN-13: 978-0321979018
課外学習等(授業時間外学習の指示)
Study Load(Self-directed Learning Outside Course Hours)
This class is a conbination of lectures and practical training. It is extremely important for the students to repeat the same practical training at home.
注意事項
Notice for Students
使用言語
Language(s) for Instruction & Discussion
English
授業開講形態等
Lecture format, etc.
対⾯・遠隔(同時双方向型)の併⽤。遠隔授業は Teams、Zoom等で⾏う。
※履修登録後に授業形態等に変更がある場合には、NUCTの授業サイトで案内します。
Combination of face-to-face and remote (interactive communication class) classes. Remote classes are conducted via Teams, Zoom, etc.
*Guidance will be posted on NUCT if there are any changes in the class format, etc. after registration.
遠隔授業(オンデマンド型)で行う場合の追加措置
Additional measures for remote class (on-demand class)