| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
The objectives of this course are to:
- understand its own topic to adopt the suitable study method
- based on its method to design a suitable survey for the investigation
- students know how to do the data management by data cleaning and decoding from words
- after data cleaning, students should know how to examine the data and obtain their outcomes
- students should know how to explain their data outcomes
- students should be able to write the report correctly
|
| 1.To enhance students’ ability with agriculture and food industry industry management. |
| 2.To enhance students’ ability with problem solving and logic reasoning. |
| 3.To enhance students’ communication skills. |
| 4.To enhance students’ ability in international bio-industry management. |
| 5.To enhance students’ self-directed learning ability. |
|
|
| Exercises |
| Practicum |
| Lecturing |
|
| Oral Presentation |
| Assignment |
| Written Presentation |
| Attendance |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Course Introduction |
| Week 2 |
Introduction of Data Type: Primary and Secondary Data, and Where to find the Data Source |
| Week 3 |
Data Collection, Sorting, Setting, Management for R |
| Week 4 |
Introduction R-Studio Environment and the Advantages of Using R |
| Week 5 |
Introduction Coding and basic Syntaxes in R |
| Week 6 |
The Probability and Distribution in R |
| Week 7 |
An Introduction to R Graphics |
| Week 8 |
R Plot Extensions |
| Week 9 |
T Tests in R (One Sample, Two Samples, and Independent Groups) |
| Week 10 |
Mid-term presentation |
| Week 11 |
ANOVA Test in R |
| Week 12 |
Introduction of Correlation and Linear Regression in R |
| Week 13 |
Binary Outcome Model in R |
| Week 14 |
Multinomial & Ordered Models in R |
| Week 15 |
Count Data Model in R |
| Week 16 |
Factor Analysis in R |
| Week 17 |
Cluster Analysis in R |
| Week 18 |
Final Reports |
|
| Evaluation |
| Assignments (15%), Discussion in Class (15%), Mid-term (30%), Final Exam (40%) |
| Textbook & other References |
1. Lander, J. P. 2023. R for Everyone: Advanced Analytics and Graphics. 2nd Ed. Addison-Wesley: Pearson Education, Inc.
2. 鍾振蔚 譯。2023。精通大數據! R 語言：資料分析與應用。第二版。Lander, J. P. 作。臺北市：旗標。
3. Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. 2020. Introduction to Econometrics with R. University of Duisburg-Essen: Essen, Germany. Available at: https://www.econometrics-with-r.org/index.html
4. You may check on the UCLA Statistical Consulting website for more information:
https://stats.idre.ucla.edu/other/dae/; we may have more template data used from this website. |
| Teaching Aids & Teacher's Website |
| TBA |
| Office Hours |
| Appointments |
| Sustainable Development Goals, SDGs |
| include experience courses：N |
|