Summary and Schedule
Statistical experimental design and data analysis fundamentals provide the background needed to plan, execute, and analyze experiments effectively. Data visualization approaches aid to interpret and communicate findings. Case studies using standard experimental designs illuminate concepts and place these designs in a real-world context. After completing this course, participants will be able to develop rigorous experimental designs that produce high-quality data.
Prerequisites
Some knowledge of the R statistical programming language are needed for success in this course.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction | What is gained from good experimental design? |
| Duration: 00h 00m | 2. Essential Features of a Comparative Experiment | How are comparative experiments structured? |
| Duration: 00h 00m | 3. Experimental Design Principles | What are the core principles of experimental design? |
| Duration: 00h 00m | 4. Statistics in Data Analysis | How can information be extracted and communicated from experimental data? |
| Duration: 00h 00m | 5. Completely Randomized Designs |
What is a completely randomized design (CRD)? What are the limitations of CRD? |
| Duration: 00h 00m | 6. Completely Randomized Design with More than One Treatment Factor | How is a CRD with more than one treatment factor designed and analyzed? |
| Duration: 00h 00m | 7. Randomized Complete Block Designs | What is randomized complete block design? |
| Duration: 00h 00m | 8. Repeated Measures Designs | What is a repeated measures design? |
| Duration: 00h 00m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
FIXME