Course title
310051001
Basic Statistics

 ICHIKAWA Manabu

KOYAMA Yusuke

GOTO Yusuke

HARADA Takuya
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
IoT Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Software Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Media Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Data Science Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Mechatronics Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Architecture and Architectural Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Environmental Systems and Urban Planning Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Bioscience Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Biomedical Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Sports Engineering Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Mathematical Sciences Course DP-1・3・2 データを収集・分析・予測を行うためのデータサイエンスの基礎的知識を理解し、利用できる。
Purpose of class
”o understand the basic concepts and analytical methods in statistics.
In particular, the purposes of this class are to understand data description and summarization, probability and probability distributions, inferential statistics, and linear models and to be able to apply them to data.”
Course description
”We can obtain various kinds of data through experiments, observations, or questionnaires.
Statistics is one of the methods to extract information from such data and understand the structure behind it.
Analysis methods for such data have become increasingly important in recent years and developed in various directions.
The contents in this class are fundamental and have been used for a long time, and its understanding is essential for handling data.
Students will learn the basic concepts and analytical methods in statistics in this class.”
Goals and objectives
  1. Understand the description and summary of data.
  2. Understand probability, probability distributions, and their relationship to data.
  3. Understand and apply inferential statistics to data.
  4. Understand hypothesis testing and apply it to data.
  5. Understand linear models and apply them to data.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Assignments Mid-term exam Final exam Total.
1. 8% 12% 20%
2. 8% 12% 20%
3. 8% 6% 6% 20%
4. 8% 12% 20%
5. 8% 12% 20%
Total. 40% 30% 30% -
Evaluation method and criteria
Assignments and examinations will be based on questions of the level 2 of the Statistics Proficiency Test.

Students will be required to pass the exam with a score of 60 points or higher out of a total of 100 points (40% for the assignment and 60% for the exam).
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Data description and summary 1 Do the assignment 20minutes
2. Data Description and Summary 2 Do the assignment 20minutes
3. Probability and probability distribution 1 Do the assignment 20minutes
4. Probability and Probability Distribution 2 Do the assignment 20minutes
5. Statistical Estimation Do the assignment 20minutes
6. Distributions and Statistical Estimation 1 Do the assignment 20minutes
7. Mid-term exam Review of 1~6 150minutes
Exam Review 30minutes
8. Distributions and Statistical Estimation 2 Do the assignment 20minutes
9. Statistical Hypothesis Testing 1 Do the assignment 20minutes
10. Statistical Hypothesis Testing 2, Data Description and Summary 3 Do the assignment 20minutes
11. Linear Model Analysis1 Do the assignment 20minutes
12. Linear Model Analysis 2 Do the assignment 20minutes
13. Other Analysis Methods Do the assignment 20minutes
14. Final exam Review of 8~13 175minutes
Exam Review 30minutes
Total. - - 625minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback outside of the class (ScombZ, mail, etc.)
Textbooks and reference materials
統計学基礎　日本統計学会編　東京図書
Prerequisites
Office hours and How to contact professors for questions
  • Ask your classroom teacher
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
N/A
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A 該当しない
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Tue Mar 31 04:04:29 JST 2026