Course Syllabus

Course Information：

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

Fall Semester
Year 2025 348369 Statistics (I) 1 3.0 3 ▲ You-Jin Park 4IEM2A 48 4 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	348369	Statistics (I)	1	3.0	3	▲	You-Jin Park	4IEM2A	48	4

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2025-05-28 10:28:29

Course Objective 本課程之目的在訓練學生，使學生熟悉經常使用之統計方法與理論，能利用已知之資料整理、分析未知之現象，打好修習其他相關學科之基礎。課程內容包括：機率理論、推定、假設檢定、迴歸分析、變異數分析等。 The purpose of this course is to train the students who can be familiar with the statistics approaches and theories. Besides, they will be able to employ the collected data to analyze the unknown environments. The course includes probability theory, testing, regression analysis and variance analysis.

Course Schedule Week 1 Introduction to statistics, data Summary, and presentation Week 2 Probability, Bayesian theorem, correlation Week 3 Decision making for a single sample – Estimation, hypothesis testing Week 4 Decision making for a single sample – Inference on the mean of a population Week 5 Decision making for a single sample – Inference on the variance of a normal population Week 6 Decision making for a single sample – Inference on a population proportion Week 7 Decision making for a single sample – Goodness of fit test Week 8 Quiz, independent Study Week 9 Midterm exam (In-class, 1 Cheat Sheet, Calculator) Week 10 Decision making for two samples – Inference on the means of two populations (Part I) Week 11 Decision making for two samples – Inference on the means of two populations (Part II) Week 12 Decision making for two samples – Paired t-test, inference on the ratio of variances of two normal populations Week 13 Decision making for two samples – Inference on two population proportions Week 14 Building Empirical Models – Simple Linear Regression (Part I) Week 15 Building Empirical Models – Simple Linear Regression (Part II) Week 16 Building Empirical Models – Simple Linear Regression (Part III) Week 17 Quiz, independent Study Week 18 Final exam (In-Class, 1 Cheat sheet, Calculator)

Evaluation and grading policy 1. Midterm Exam: 30 % 2. Final Exam: 35 % 3. Quiz: 35 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1)

The access to curricular consultation Email or i-school system.
Office hour: Thursday 11:10 ~ 12:00.

I will notify students of the lecture schedules through the email and Line. And, more details on ANOVA (analysis of variance) will be given.

Expanding teaching and resources

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

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

Note
Email and MS Teams

I will notify students of the lecture schedules through the email and Line. And, more details on ANOVA (analysis of variance) will be given.

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2025-05-28 10:28:29
Course Objective	本課程之目的在訓練學生，使學生熟悉經常使用之統計方法與理論，能利用已知之資料整理、分析未知之現象，打好修習其他相關學科之基礎。課程內容包括：機率理論、推定、假設檢定、迴歸分析、變異數分析等。 The purpose of this course is to train the students who can be familiar with the statistics approaches and theories. Besides, they will be able to employ the collected data to analyze the unknown environments. The course includes probability theory, testing, regression analysis and variance analysis.
Course Schedule	Week 1 Introduction to statistics, data Summary, and presentation Week 2 Probability, Bayesian theorem, correlation Week 3 Decision making for a single sample – Estimation, hypothesis testing Week 4 Decision making for a single sample – Inference on the mean of a population Week 5 Decision making for a single sample – Inference on the variance of a normal population Week 6 Decision making for a single sample – Inference on a population proportion Week 7 Decision making for a single sample – Goodness of fit test Week 8 Quiz, independent Study Week 9 Midterm exam (In-class, 1 Cheat Sheet, Calculator) Week 10 Decision making for two samples – Inference on the means of two populations (Part I) Week 11 Decision making for two samples – Inference on the means of two populations (Part II) Week 12 Decision making for two samples – Paired t-test, inference on the ratio of variances of two normal populations Week 13 Decision making for two samples – Inference on two population proportions Week 14 Building Empirical Models – Simple Linear Regression (Part I) Week 15 Building Empirical Models – Simple Linear Regression (Part II) Week 16 Building Empirical Models – Simple Linear Regression (Part III) Week 17 Quiz, independent Study Week 18 Final exam (In-Class, 1 Cheat sheet, Calculator)
Evaluation and grading policy	1. Midterm Exam: 30 % 2. Final Exam: 35 % 3. Quiz: 35 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1)
The access to curricular consultation	Email or i-school system. Office hour: Thursday 11:10 ~ 12:00. I will notify students of the lecture schedules through the email and Line. And, more details on ANOVA (analysis of variance) will be given.
Expanding teaching and resources
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note	Email and MS Teams I will notify students of the lecture schedules through the email and Line. And, more details on ANOVA (analysis of variance) will be given.