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 348368 Statistics (I) Practice 1 1.0 2 ▲ You-Jin Park 4IEM2A 47 0 計中電腦教室

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	348368	Statistics (I) Practice	1	1.0	2	▲	You-Jin Park	4IEM2A	47	0	計中電腦教室

Course Plan：

Instructor You-Jin Park　Office Hours

Email eugenepark993@gmail.com

Last Updated 2025-05-28 10:27:33

Course Objective This course is an introduction to statistical computing and will be taught in Python or Minitab. It is assumed students have exposure to basic statistics and knowledge of calculus (i.e., differential and integral calculus). The main focus of the class will be on statistical analysis and graphics in Python or Minitab. Students will cover basic statistical concepts and various statistical methods such as estimation, hypothesis tests, regression analysis, and ANOVA.

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

Evaluation and grading policy Midterm Exam: 20 % Final Exam: 30 % Assignments: 20 % Team project: 20 % Attendance: 10 %

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

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
The course link will be sent by the school E-mail.

Instructor	You-Jin Park　Office Hours
Email	eugenepark993@gmail.com
Last Updated	2025-05-28 10:27:33
Course Objective	This course is an introduction to statistical computing and will be taught in Python or Minitab. It is assumed students have exposure to basic statistics and knowledge of calculus (i.e., differential and integral calculus). The main focus of the class will be on statistical analysis and graphics in Python or Minitab. Students will cover basic statistical concepts and various statistical methods such as estimation, hypothesis tests, regression analysis, and ANOVA.
Course Schedule	Week 1 Introduction to statistics, data Summary, and presentation Week 2 Probability, Bayesian theorem, correlation Week 3 Random variables and probability distributions – Random variables, probability Week 4 Random variables and probability distributions – Continuous random variables Week 5 Random variables and probability distributions – Discrete random variables Week 6 Decision making for a single sample – Estimation, hypothesis testing Week 7 Decision making for a single sample – Inference on the mean of a population Week 8 Quiz, independent Study Week 9 Midterm exam (In-class, 1 Cheat Sheet, Calculator) Week 10 Decision making for a single sample – Inference on the variance of a normal population Week 11 Decision making for a single sample – Inference on a population proportion, goodness of fit test Week 12 Decision making for two samples – Inference on the means of two populations (Part I) Week 13 Decision making for two samples – Inference on the means of two populations (Part II) Week 14 Decision making for two samples – Paired t-test, inference on the ratio of variances of two normal populations Week 15 Decision making for two samples – Inference on two population proportions Week 16 ANOVA Week 17 Quiz, independent Study Week 18 Final exam
Evaluation and grading policy	Midterm Exam: 20 % Final Exam: 30 % Assignments: 20 % Team project: 20 % Attendance: 10 %
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
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	The course link will be sent by the school E-mail.