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	362351	Engineering Statistics	1	3.0	3	★	Chi-Yun Liu	4CE2A 4CE2B	40	0	土木二甲乙合開

Course Plan：

Instructor	Chi-Yun Liu　Office Hours
Email	cyliu@mail.ntut.edu.tw
Last Updated	2026-06-04 15:56:32
Course Objective	本課程介紹統計學與機率論的基本概念，包括敘述統計、機率、隨機變數、常見機率分配、抽樣分配，以及基本推論方法。學生將學習如何整理、描述、分析資料，並理解統計方法在實際問題中的應用。 This course introduces fundamental concepts in statistics and probability, including descriptive statistics, probability, random variables, common probability distributions, sampling distributions, and basic inference. Students will learn to organize, describe, and analyze data, and understand applications of statistical methods.
Course Schedule	Chapter 1: Introduction to Statistics and Probability Chapter 2: Measures of Central Tendency Chapter 3: Measures of Dispersion Chapter 4: Skewness and Kurtosis Chapter 5: Fundamentals of Probability Chapter 6: Conditional Probability and Independence Chapter 7: Random Variables Chapter 8: Midterm Examination Chapter 9: Joint and Marginal Distributions Chapter 10: Special Discrete Distributions Chapter 11: Normal Distribution Chapter 12: Sampling Distributions Chapter 13: Chi-square, t, and F Distributions Chapter 14: Gamma Distribution Chapter 15: Linear Transformations and Subscores Chapter 16: Final Examination or Final Project Presentation
Evaluation and grading policy	Midterm examination:35% Final examination or project: 35% Attendance and participation: 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References (for supplementary reading, not the main textbook) Ross, S. M. A First Course in Probability. Pearson. Devore, J. L. Probability and Statistics for Engineering and the Sciences. Cengage Learning.
The access to curricular consultation	Write email to make appointments
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 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） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG10：減少不平等（Reduced Inequalities） SDG12：負責任的消費與生產（Responsible Consumption and Production）
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） ● 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	Flexible Teaching Week: Students will engage in independent study and project related activities. Assessment may be incorporated into project performance or course participation. Details will be announced during the semester.