GISDepartment of Global and Interdisciplinary Studies
PRI100ZA（情報学基礎 / Principles of informatics 100）Introduction to StatisticsIntroduction to Statistics
Adam Randall Smith
授業コードなどClass code etc
| 学部・研究科Faculty/Graduate school | GISDepartment of Global and Interdisciplinary Studies |
| 添付ファイル名Attached documents | |
| 年度Year | 2025 |
| 授業コードClass code | A6052 |
| 旧授業コードPrevious Class code | A6500 |
| 旧科目名Previous Class title | Statistics |
| 開講時期Term | 春学期授業/Spring |
| 曜日・時限Day/Period | 木1/Thu.1 |
| 科目種別Class Type | |
| キャンパスCampus | 市ヶ谷 / Ichigaya |
| 教室名称Classroom name | 情報実習室H（0402） |
| 配当年次Grade | 1～4 |
| 単位数Credit(s) | 2 |
| 備考（履修条件等）Notes | Not Available for ESOP Students. |
| 他学部公開科目Open Courses | |
| 他学部公開（履修条件等）Open Courses (Notes) | |
| グローバル・オープン科目Global Open Courses | |
| 成績優秀者の他学部科目履修制度対象Interdepartmental class taking system for Academic Achievers | ○ |
| 成績優秀者の他学部科目履修（履修条件等）Interdepartmental class taking system for Academic Achievers (Notes) | 制度ウェブサイトの3．科目別の注意事項(1)GIS主催科目の履修上の注意を参照すること。 |
| 実務経験のある教員による授業科目Class taught by instructors with practical experience | |
| SDGsCPSDGs CP | |
| アーバンデザインCPUrban Design CP | |
| ダイバーシティCPDiversity CP | |
| 未来教室CPLearning for the Future CP | |
| カーボンニュートラルCPCarbon Neutral CP | |
| 千代田コンソ単位互換提供（他大学向け）Chiyoda Campus Consortium | |
| 旧科目との重複履修Duplicate Subjects Taken Under Previous Class Title | × |
| カテゴリー（2024年度以降入学者）Category (commenced 2024 onwards) |
100-level General Study Courses Academic Skills Courses（Elective：選択） |
| カテゴリー（2020～2023年度入学者）Category (commenced 2020-2023) | |
| カテゴリー（2016～2019年度入学者）Category (commenced 2016-2019) |
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授業の概要と目的（何を学ぶか）Outline and objectives
This course introduces students to fundamental statistical concepts and methods used in data analysis. Students will learn how to organize, summarize, and interpret data using statistical techniques. The course covers descriptive and inferential statistics, including probability, hypothesis testing, and correlation. The focus is on real-world applications in a variety of disciplines, preparing students to use statistical reasoning in decision-making and research.
到達目標Goal
By the end of this course, students should be able to:
1. Understand and apply descriptive statistics, including measures of central tendency and variability.
2. Interpret data visualizations such as histograms, scatter plots, and bar graphs.
3. Conduct basic inferential statistical tests, including t-tests and regression analysis.
4. Use probability concepts to make informed decisions.
5. Differentiate between population vs. sample and understand the significance of sample distributions.
6. Apply statistical reasoning in various fields, including business, social sciences, and health sciences.
7. Gain familiarity with statistical processing software, especially Microsoft Excel, R, and ChatGPT.
8. Enjoy statistics; if you can truly understand basic statistics, you will see the world in a new and beautiful way!
この授業を履修することで学部等のディプロマポリシーに示されたどの能力を習得することができるか（該当授業科目と学位授与方針に明示された学習成果との関連）Which item of the diploma policy will be obtained by taking this class?
Will be able to gain “DP 1”, “DP 2”, and “DP 4”.
授業で使用する言語Default language used in class
英語 / English
授業の進め方と方法Method(s)（学期の途中で変更になる場合には、別途提示します。 /If the Method(s) is changed, we will announce the details of any changes. ）
This course uses a lecture-based approach combined with interactive problem-solving activities. Students will engage in real-world examples, in-class exercises, and discussions. The use of statistical software and spreadsheets will be introduced but a full understanding of these programs is not required (i.e., you will only be taught as much as is necessary for each topic). Homework assignments reinforce concepts covered in class, and students are encouraged to actively participate and ask questions.
アクティブラーニング（グループディスカッション、ディベート等）の実施Active learning in class (Group discussion, Debate.etc.)
あり / Yes
フィールドワーク（学外での実習等）の実施Fieldwork in class
なし / No
授業計画Schedule
授業形態/methods of teaching：対面/face to face
※各回の授業形態は予定です。教員の指示に従ってください。
1[対面/face to face]：Introduction to Statistics & Course Overview
What is statistics? Why does it matter? Overview of course structure, grading, and key concepts. Basic introduction to data types.
2[対面/face to face]：Descriptive Statistics (1)
Understanding measures of central tendency (mean, median, mode) and their significance. Data visualization techniques (histograms, box plots).
3[対面/face to face]：Descriptive Statistics (2) & Variability
Understanding variability: range, interquartile range (IQR), variance, and standard deviation. The importance of data spread.
4[対面/face to face]：Correlation & Scatter Plots
Understanding relationships between two variables. How to interpret scatter plots and calculate correlation coefficients.
5[対面/face to face]：Population vs. Sample & Probability Basics
Introduction to probability concepts. Understanding populations, samples, and the importance of representative sampling.
6[対面/face to face]：Probability Distributions & Z-scores
Normal distribution, standardization, and Z-scores. How to compare individual scores to a population.
7[対面/face to face]：Hypothesis Testing: Concepts & Framework
Understanding null vs. alternative hypotheses, Type I & Type II errors, and p-values. Interpreting statistical significance.
8[対面/face to face]：Midterm Exam (In-Class)
Assessment covering descriptive statistics, probability, correlation, and hypothesis testing. Designed to gauge progress before moving into inferential statistics.
9[対面/face to face]：T-tests (1): Understanding Mean Comparisons
When and why we use t-tests. Introduction to one-sample and independent-samples t-tests.
10[対面/face to face]：T-tests (2): Paired Samples & Applications
Understanding paired t-tests and how they apply to repeated measures. Practical examples and real-world applications.
11[対面/face to face]：Regression Analysis (1): Understanding Relationships
Introduction to simple linear regression. How to interpret regression equations and coefficients.
12[対面/face to face]：Regression Analysis (2): Multiple Regression & Predictions
Expanding to multiple predictors. Interpreting R-squared, coefficients, and making data-driven predictions.
13[対面/face to face]：Common Statistical Misconceptions & Real-World Applications
Exploring how statistics are misused in media, politics, and research. Common pitfalls in data interpretation and how to spot misleading statistics.
14[対面/face to face]：Preparing for the Final Exam
Review of key topics. Practice problems, common mistakes, and strategies for success on the Final Exam. Q&A session.
授業時間外の学習（準備学習・復習・宿題等）Work to be done outside of class (preparation, etc.)
Students are expected to complete weekly homework assignments that reinforce key concepts covered in class. These assignments will involve problem-solving, data analysis, and interpretation of statistical results. Additionally, students are encouraged to review their lecture notes and handouts after each class.
Students should expect to spend at least 2 hours per week on assignments, reviewing concepts, and practicing problems outside of class.
Regular practice is essential for developing a solid understanding of statistical methods and their applications.
テキスト（教科書）Textbooks
No textbook will be used. Handouts and reading materials will be provided by lecturer.
参考書References
References will be introduced in class.
成績評価の方法と基準Grading criteria
Students will be evaluated on the basis of active participation and assignments given in each class (40%). There will also be two exams, a Midterm Exam (30%) and Final Exam (30%). The purpose of the Midterm Exam is to assure you are learning at a suitable pace, and identify any potential learning problems early.
No credit will be given to students with more than two unexcused absences.
学生の意見等からの気づきChanges following student comments
Not applicable
その他の重要事項Others
This course is strongly recommended for students interested in various disciplines in the social sciences. It will be particularly useful for students intending to conduct their own research as part of a seminar, psychology courses, or related fields.
Additionally, students will be encouraged to use AI tools such as ChatGPT and statistical software to enhance their learning. AI can assist with data visualization, summarizing concepts, and generating study resources. However, its use should remain supplementary—not a replacement for critical thinking and problem-solving.
Rules regarding AI use:
1. AI-generated responses must be fact-checked and not used blindly.
2. AI tools may not be used during exams.
3. If AI is used, students must cite AI contributions in assignments (e.g., "ChatGPT was used for initial topic brainstorming").
By engaging critically with both statistical methods and modern tools such as AI, students will develop a deeper understanding of data analysis and research design while strengthening their ability to navigate technological advancements in the field.
Final note: I am an Assistant Professor of Psychology at ICU; I typically teach over 150 students in my Introductory Statistics Courses. Therefore, I am very much looking forward to teaching a smaller group of students at Hosei! Either way, please feel free to reach out to me any time.
Prerequisite
None.