查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-2 355086 統計學(二) 1 3.0 3 ▲ 張漢利工管二乙 68 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	355086	統計學(二)	1	3.0	3	▲	張漢利	工管二乙	68	0

教學大綱與進度：

教師姓名張漢利　教師諮商時間(Office Hours)

Email hendri@mail.ntut.edu.tw

最後更新時間 2026-02-23 08:17:11

課程大綱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.

課程進度 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)

評量方式與標準 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.

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Douglas C. Montgomery and George C. Runger (2018), Applied Statistics and Probability for Engineers, 7th Edition, John Wiley. ISBN 978-1-119-40036-3

課程諮詢管道 Email and Office Hours. Consultations are available during the instructor’s announced office hours. Prior email appointment is recommended.

延伸教學與資源 ● 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.

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入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）

備註

教師姓名	張漢利　教師諮商時間(Office Hours)
Email	hendri@mail.ntut.edu.tw
最後更新時間	2026-02-23 08:17:11
課程大綱	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.
課程進度	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)
評量方式與標準	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.
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Douglas C. Montgomery and George C. Runger (2018), Applied Statistics and Probability for Engineers, 7th Edition, John Wiley. ISBN 978-1-119-40036-3
課程諮詢管道	Email and Office Hours. Consultations are available during the instructor’s announced office hours. Prior email appointment is recommended.
延伸教學與資源	● 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.
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入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）
備註