CRES 720 Biostatistics I

This course covers the theoretical foundations of biostatistics and the associated computational approaches of exploratory and descriptive biostatistical methods for the analysis of data in clinical research (observational and experimental). These methods are based on probability theory and include assessing the impact of chance and variability on the interpretation of research findings. Topics include probability theory, measurement theory, descriptive and exploratory analysis. Students will utilize a statistical programming language to apply theoretical topics with real world data and will get hands on practice through an exploratory data analysis project including loading and transforming data, exploratory analysis, and visualization.

Credits

3

Student Learning Outcomes

1. 1. Understand the role biostatistics serves in clinical research.
2. 2. Apply concepts of probability, random variation and commonly used statistical probability distributions.
3. 3. Distinguish among the different measurement scales and the implications for selection of statistical methods.
4. 4. Apply descriptive techniques and models commonly used to summarize clinical research data.
5. 5. Understand foundational statistical programming concepts in R ( https://cran.r-project.org/) with an understanding of the generality of these concepts across available platforms (SAS, SPSS, STATA, Python )
6. 6. Complete an exploratory data analysis project that includes, but is not limited to, statistical programming for: i. Importing data from native file types (such as text, comma or tab delimited) ii. Creating tidy data sets (Formatting, merging, subsetting, manipulation) iii. Transforming data (create new variables, classify, model) iv. Performing numerical, statistical and graphical exploration of data