Syllabus and Course Policy
Syllabus
General Information
Course Objectives
To train students in basic statistical knowledge and econometric concepts.
To enhance students’ familiarity with data analysis and provide hands-on experience in statistical analysis.
Textbooks
- Bluman, A. G. (2012). Elementary Statistics: A Step-by-Step Approach (8th ed.). McGraw-Hill.
Course Structure
Lecture (First hour): Foundational knowledge and concepts introduced each session.
Lab (Second hour): Hands-on in-class exercises using R to apply the concepts learned during the lecture.
Assessment Criteria
| Component | Weight | Details |
|---|---|---|
| In-Class Exercises | 36% | 12 exercises × 3% each |
| Midterm Exam | 32% | |
| Final Exam | 32% |
Regulations
- Attendance
Per Shih Chien University regulations, students who are absent for more than one-third of class sessions will receive a final grade of zero. Make-up attendance will not be permitted.
- In-class exercise deadline
The deadline for all in-class exercises is before midnight of the following week. Late submissions will not be accepted.
- Academic Integrity
All submitted work must be the student’s own. Copying from other students is strictly prohibited. AI detection tools are used to review submissions; any work found to be copied will receive a grade of zero.
- AI Policy
Students may use AI tools (e.g., ChatGPT or Gemini) as learning aids for in-class exercises, but must be able to explain and defend any code or analysis they submit. Uncritical submission of AI-generated work without understanding constitutes academic dishonesty. AI tools are not permitted during examinations.
Exam Regulations
The examination is conducted online under the following regulations.
- Mandatory Attendance
Students must take the exam in person. Roll call will be taken five minutes before the exam begins. Late arrival or absence will result in a score of zero.
- Network Restriction
The exam system tracks IP addresses. You must use the campus Wi-Fi. Access from a non-campus IP address will result in a score of zero.
- One Device Only
The exam must be taken on a single device. Keeping any additional device, such as a phone or a tablet, available during the exam will result in a score of zero.
- Authorized Resources
This is an open-book exam. Printed reference materials are permitted without limit. Electronic reference materials and AI tools, such as ChatGPT or Gemini, are prohibited. Any use will result in a score of zero.
- Media Prohibition
Photography, video or audio recording, and screenshotting are strictly prohibited. Violators will receive a score of zero.
- No Communication or Disturbance
Interpersonal communication, causing noise or disruptive movements are strictly prohibited. Violators will receive a score of zero.
- Copy-Paste Disabled
The exam system prohibits copying and pasting. Do not attempt to bypass this feature.
Required Software
All software is free and open-source: R at https://cran.r-project.org, RStudio Desktop at https://posit.co/download/rstudio-desktop, and R packages tidyverse, ggplot2, patchwork (installation instructions on the course R page).
Statistics (1)
Course information
| Lecturer: Yu-You Liou | Contact: d10627008@ntu.edu.tw |
Course Code: EIB-21E-01-A1 |
Website: https://yyliou.github.io/stat |
| Meeting Time: Wednesday, Periods 6–7 (13:10–15:00) | Location: A207 |
Course Schedule
| Week | Topic |
|---|---|
| 1 | Explain the syllabus and class regulations |
| 2 | Chapter 1: The nature of probability and statistics (ex1) |
| 3 | Chapter 2: Frequency distributions and graphs (ex2) |
| 4 | Chapter 3: Data description (ex3) |
| 5 | Chapter 3: Data description (ex4) |
| 6 | Chapter 4: Probability and counting rules (ex5) |
| 7 | Chapter 4: Probability and counting rules (ex6) |
| 8 | Midterm review |
| 9 | Midterm exam |
| 10 | Chapter 5: Discrete probability distributions (ex7) |
| 11 | Chapter 5: Discrete probability distributions (ex8) |
| 12 | Chapter 6: The normal distribution (ex9) |
| 13 | Chapter 6: The normal distribution (ex10) |
| 14 | Chapter 7: Confidence intervals and sample size (ex11) |
| 15 | Chapter 7: Confidence intervals and sample size (ex12) |
| 16 | Final exam |
| 17 | Flexible learning week |
| 18 | Flexible learning week |
Statistics (2)
Course information
| Lecturer: Yu-You Liou | Contact: d10627008@ntu.edu.tw |
Course Code: EIB-21F-01-A1 |
Website: https://yyliou.github.io/stat |
| Meeting Time: Thursday, Periods 6–7 (13:10–15:00) | Location: L309 |
Course Schedule
| Week | Topic |
|---|---|
| 1 | Explain the syllabus and class regulations |
| 2 | Chapter 8: Hypothesis testing (ex1) |
| 3 | Chapter 8: Hypothesis testing (ex2) |
| 4 | Chapter 8: Hypothesis testing (ex3) |
| 5 | Chapter 9: Testing the difference between two means, two proportions, and two variances (ex4) |
| 6 | Chapter 9: Testing the difference between two means, two proportions, and two variances (ex5) |
| 7 | Chapter 10: Correlation and regressions (ex6) |
| 8 | Midterm review |
| 9 | Midterm exam |
| 10 | Chapter 10: Correlation and regression (ex7) |
| 11 | Chapter 10: Correlation and regression (ex8) |
| 12 | Chapter 11: Other chi-square tests (ex9) |
| 13 | Chapter 11: Other chi-square tests (ex10) |
| 14 | Chapter 12: Analysis of variance (ex11) |
| 15 | Chapter 12: Analysis of variance (ex12) |
| 16 | Final exam |
| 17 | Flexible learning week |
| 18 | Flexible learning week |
Note: Syllabus is subject to change. Any updates will be announced on the course website.