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 355085 Statistics (II) Practice 1 1.0 2 ▲ Hendri Sutrisno 4IEM2B 60 0 計網電腦教室

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	355085	Statistics (II) Practice	1	1.0	2	▲	Hendri Sutrisno	4IEM2B	60	0	計網電腦教室

Course Plan：

Instructor Hendri Sutrisno　Office Hours

Email hendri@mail.ntut.edu.tw

Last Updated 2025-12-18 09:58:40

Course Objective 課程目的在於培養學生運用統計軟體進行資料分析與統計推論的實務能力，並透過實作與討論深化對統計方法的理解。課程強調以互動、說明與同儕學習作為學習歷程的一部分，鼓勵學生透過解釋、提問與討論來建構統計思維。課程內容配合統計推論之核心概念，透過 Python 與 R 進行操作、模擬、診斷與結果解讀。 The course aims to develop students’ ability to apply statistical tools for data analysis and statistical inference through hands-on practice. Learning emphasizes interaction, explanation, and peer learning as integral parts of the process. Students are encouraged to build statistical understanding by discussing, explaining, and questioning methods and results. Course activities align with core concepts of statistical inference and are implemented using Python and R for computation, simulation, diagnostics, and interpretation.

Course Schedule Week 1: Course orientation and software setup Week 2: Joint distributions and correlation Week 3: Random variables and simulation Week 4: Descriptive statistics Week 5: Sampling distributions Week 6: Point estimation and standard error Week 7: Confidence intervals Week 8: Hypothesis testing Week 9: Two-sample inference Week 10: Nonparametric methods Week 11: Simple linear regression Week 12: Regression diagnostics Week 13: Introduction to multiple regression Week 14: Integrated simulation and inference practice Week 15: Comprehensive lab practice Weeks 16–18: Flexible use for review or assessment

Evaluation and grading policy In-Class Lab Assignments: 55% Interaction and Peer Teaching: 35% Learning Engagement: 10%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Rigdon, S. E., Fricker, Jr, R. D., and Montgomery, D. C. (2024). Introduction to Probability and Statistics for Data Science: with R. Cambridge: Cambridge University Press.

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

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）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
　

Instructor	Hendri Sutrisno　Office Hours
Email	hendri@mail.ntut.edu.tw
Last Updated	2025-12-18 09:58:40
Course Objective	課程目的在於培養學生運用統計軟體進行資料分析與統計推論的實務能力，並透過實作與討論深化對統計方法的理解。課程強調以互動、說明與同儕學習作為學習歷程的一部分，鼓勵學生透過解釋、提問與討論來建構統計思維。課程內容配合統計推論之核心概念，透過 Python 與 R 進行操作、模擬、診斷與結果解讀。 The course aims to develop students’ ability to apply statistical tools for data analysis and statistical inference through hands-on practice. Learning emphasizes interaction, explanation, and peer learning as integral parts of the process. Students are encouraged to build statistical understanding by discussing, explaining, and questioning methods and results. Course activities align with core concepts of statistical inference and are implemented using Python and R for computation, simulation, diagnostics, and interpretation.
Course Schedule	Week 1: Course orientation and software setup Week 2: Joint distributions and correlation Week 3: Random variables and simulation Week 4: Descriptive statistics Week 5: Sampling distributions Week 6: Point estimation and standard error Week 7: Confidence intervals Week 8: Hypothesis testing Week 9: Two-sample inference Week 10: Nonparametric methods Week 11: Simple linear regression Week 12: Regression diagnostics Week 13: Introduction to multiple regression Week 14: Integrated simulation and inference practice Week 15: Comprehensive lab practice Weeks 16–18: Flexible use for review or assessment
Evaluation and grading policy	In-Class Lab Assignments: 55% Interaction and Peer Teaching: 35% Learning Engagement: 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Rigdon, S. E., Fricker, Jr, R. D., and Montgomery, D. C. (2024). Introduction to Probability and Statistics for Data Science: with R. Cambridge: Cambridge University Press.
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
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） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note