Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 355086 Statistics (II) 1 3.0 3 ▲ Hendri Sutrisno 4IEM2B 68 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	355086	Statistics (II)	1	3.0	3	▲	Hendri Sutrisno	4IEM2B	68	0

Course Plan：

Instructor Hendri Sutrisno　Office Hours

Email hendri@mail.ntut.edu.tw

Last Updated 2026-02-23 08:17:11

Course Objective 本課程旨在培養學生運用統計推論分析資料變異與不確定性的能力，以支援分析、評估與決策過程。課程重視推論邏輯、假設條件與結果詮釋，強調統計方法在資料分析與系統性問題解決中的應用。課程內容涵蓋統計推論之核心理論與方法，並透過概念說明、課堂討論、小組解題、案例分析與引導式活動，協助學生建立完整的統計思維。 This course aims to develop students’ ability to analyze data variability and uncertainty using statistical inference to support analysis, evaluation, and decision-making. The course emphasizes inferential logic, assumptions, and interpretation. It highlights the role of statistical methods in data analysis and systematic problem solving. Course content covers core theories and methods of statistical inference, delivered through conceptual explanations, discussions, guided problem-solving, case analysis, and structured in-class activities.

Course Schedule Week 1: Course orientation and learning approach Weeks 2–4: Joint probability distributions and correlation Weeks 5–6: Common joint distributions and functions of random variables Weeks 7–8: Descriptive statistics and data visualization Weeks 9–10: Point estimation and sampling distributions Weeks 11–12: Confidence intervals Weeks 13–14: Hypothesis testing Weeks 15–16: Statistical inference for two samples Week 17-18: Regression analysis (simple and multiple)

Evaluation and grading policy Weekly module quizzes: 25% Exams: 40% Final project: 25% Guided practice: 10% Class activities may include short discussions, group problem-solving, case analysis, concept checks, and guided exercises. Participation is evaluated based on engagement and reasoning.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Douglas C. Montgomery and George C. Runger (2018), Applied Statistics and Probability for Engineers, 7th Edition, John Wiley. ISBN 978-1-119-40036-3

The access to curricular consultation Email and Office Hours. Consultations are available during the instructor’s announced office hours. Prior email appointment is recommended.

Expanding teaching and resources ● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）

Note
　

Instructor	Hendri Sutrisno　Office Hours
Email	hendri@mail.ntut.edu.tw
Last Updated	2026-02-23 08:17:11
Course Objective	本課程旨在培養學生運用統計推論分析資料變異與不確定性的能力，以支援分析、評估與決策過程。課程重視推論邏輯、假設條件與結果詮釋，強調統計方法在資料分析與系統性問題解決中的應用。課程內容涵蓋統計推論之核心理論與方法，並透過概念說明、課堂討論、小組解題、案例分析與引導式活動，協助學生建立完整的統計思維。 This course aims to develop students’ ability to analyze data variability and uncertainty using statistical inference to support analysis, evaluation, and decision-making. The course emphasizes inferential logic, assumptions, and interpretation. It highlights the role of statistical methods in data analysis and systematic problem solving. Course content covers core theories and methods of statistical inference, delivered through conceptual explanations, discussions, guided problem-solving, case analysis, and structured in-class activities.
Course Schedule	Week 1: Course orientation and learning approach Weeks 2–4: Joint probability distributions and correlation Weeks 5–6: Common joint distributions and functions of random variables Weeks 7–8: Descriptive statistics and data visualization Weeks 9–10: Point estimation and sampling distributions Weeks 11–12: Confidence intervals Weeks 13–14: Hypothesis testing Weeks 15–16: Statistical inference for two samples Week 17-18: Regression analysis (simple and multiple)
Evaluation and grading policy	Weekly module quizzes: 25% Exams: 40% Final project: 25% Guided practice: 10% Class activities may include short discussions, group problem-solving, case analysis, concept checks, and guided exercises. Participation is evaluated based on engagement and reasoning.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Douglas C. Montgomery and George C. Runger (2018), Applied Statistics and Probability for Engineers, 7th Edition, John Wiley. ISBN 978-1-119-40036-3
The access to curricular consultation	Email and Office Hours. Consultations are available during the instructor’s announced office hours. Prior email appointment is recommended.
Expanding teaching and resources	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
Note