Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	367089	Statistics with Recitation	1	3.0	3	★	Hendri Sutrisno	Distance Education (U)	0	0	台大李宗穎https://ppt.cc/fCH4qx

Course Plan：

Instructor	Hendri Sutrisno　Office Hours
Email	hendri@mail.ntut.edu.tw
Last Updated	2026-08-08 21:06:49
Course Objective	This course introduces fundamental statistical concepts and analytical tools for data-driven decision-making. Core topics cover descriptive statistics, data visualization, basic probability theory, random variable distributions, inferential statistics (hypothesis testing and confidence intervals for numerical and categorical data), and simple linear regression. The curriculum emphasizes building conceptual intuition and practical application in English. This course is offered under the Taiwan AI College Alliance (TAICA) program. Lectures are delivered asynchronously by NTU faculty via NTU COOL.
Course Schedule (Week 1-16)	* Course Introduction & Data Fundamentals * Data Types, Sampling Methods, and Experimental Design * Summarizing Data Numerical & Graphical Representations * Basic Probability Rules, Conditional Probability, and Independence * Random Variables, Normal Distribution, and Binomial Distribution * Sampling Distributions and Central Limit Theorem * Confidence Intervals and Point Estimates * Midterm Exam (Synchronized, In-Person 9:30 AM – 12:00 PM) * Hypothesis Testing for Single Categorical Proportions * Chi-Square Tests for Independence and Goodness-of-Fit * Hypothesis Testing & Confidence Intervals for Single Means * Comparing Two Independent Means and Paired Data Analysis * Introduction to Simple Linear Regression & Correlation * Model Fitting, Least Squares Method, and Residual Analysis * Final Exam (Synchronized, In-Person 9:30 AM – 12:00 PM) * Course Review & Final Grade Assessment
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) ● 線上數位教材學習(Coursera、edX、磨課師等)(Online digital learning (Coursera, edX, MOOCs, etc.)) ● AI 輔助學習與工具應用(AI-assisted learning and tool application) Content Weeks 17 and 18 focus on statistical problem-solving, regression model interpretation, and data analysis synthesis. Students will work on guided problem sets involving probability calculations, confidence interval construction, and linear regression evaluations Hours 6 Learning Outcomes Student performance is evaluated through practice problem submissions, participation in statistical exercise discussions, and self-review reports on regression and inference concepts. Assessment Percentage 0%
Evaluation and grading policy	- Midterm Exam: 40% - Final Exam: 60%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Required Textbook: Diez, D. M., Barr, C. D., & Çetinkaya-Rundel, M. (2019). OpenIntro Statistics (4th ed.). Supplementary Reference: Rosling, H., Rosling, O., & Rönnlund, A. R. (2018). Factfulness: Ten Reasons We're Wrong About the World.
The access to curricular consultation	Local student support and consultation are available during scheduled office hours, via email, and through the assigned Teaching Assistant
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	Course content covers descriptive statistics, probability distributions, statistical inference, and linear regression. Instruction is fully in English.