Course Description:
This one-credit Graduate Seminar in Frequency Data Analysis focuses on key statistical concepts and techniques for handling frequency data. Participants will learn how to analyze and interpret frequency data, apply descriptive statistics, conduct hypothesis testing using the Chi-squared test, and leverage Python as a tool for data analysis.
By the end of this seminar, students will be able to:
- Analyze and interpret frequency data effectively.
- Apply descriptive statistics to summarize, visualize and communicate findings.
- Conduct hypothesis testing using the Chi-squared test and Python. ** Evaluation
- Attendance (20%)
- Assignments (80%)
| 🌱 Padlet: inclass activity | 🌱 Class log |
| Week | Date | Key topic(s) | Description | Course materials | Suppl. |
|---|---|---|---|---|---|
| Meeting 01 | Mar.14(V:1h) Mar.21(Z:2h) |
Introduction, Descriptive statistics I |
Course overview, Why statistics? Steps of statistical approach, Types of data, Software | [Video01 LMS] Lecture01, Coding01 |
dataformat |
| Meeting 02 | Apr.4(V) Apr.11(Z) |
Descriptive Statistics II | Purpose, Key measures, Visualize data I | Coding02 | 🔴dataframe |
| Meeting 03 | Apr.25(V) May9(Z) |
Descriptive Statistics III, Frequency data | Visualize data II, Real data practice | Video, Chi-Squared test slides |
Practice Apr.25 |
| Meeting 04 | May16(V) May23(Z) |
Chi-squared test I | Purpose, Types of Chi-squared tests, Concepts | Video0516, Independence Goodness, Coding03 |
🔴Dataframe |
| Meeting 05 | Jun.6(V) Jun.13(Z) |
Chi-squared test II | Visualize frequency data results, Interpretation of results, Practice with examples, Assumptions and limitations | Coding03: continue | |
| Final | (Due by Jun. 23) | Final report (A squib) to submit | Data will be provided | Guideline |