CRES 721 Biostatistics II

This course covers the theoretical foundations and the associated computational approaches of inferential biostatistical methods for the analysis and presentation of data in clinical research (observational and experimental). Topics include analysis of assumptions and visualization; hypothesis testing; methods of comparison of discrete and continuous data including t-test, correlation, regression, and general linear models (including ANOVA). Students will utilize a statistical programming language to apply theoretical topics with real world data and will get hands on practice through an inferential data analysis project including model building and testing assumptions, hypothesis testing, presentation and dissemination based on reproducible research principles.

Credits

3

Student Learning Outcomes

1. 1.Describe and compare common statistical methods and models for inference.
2. 2. Choose preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
3. 3. Evaluate accuracy, precision and efficiency of statistical models and estimates.
4. 4. Interpret results of statistical analyses found in clinical research.
5. 6. Understand and Utilize principles of reproducible research throughout the data analysis project process including tools such as GitHub, Jupyter notebooks, Figshare and OSF
6. 6. Complete an inferential data analysis project that includes, but is not limited to, statistical programming for: i. Performing numerical, statistical and graphical exploration of data ii. Testing assumptions iii. Testing hypotheses iv. Presentation and dissemination that includes an interpretation of results and defense of approach