Please Mind: the complete information for this course is not provided yet, this page will be finalized in the upcoming weeks
• Demonstrate competence with the basics of the statistical programming language R including, but not limited to, loading and transforming data, finding and installing packages, applying functions, and working with scripts
• Create effective visualizations of data using R
• Use and interpret descriptive statistics to understand data using R
• Conduct and interpret means comparison-based analyses such as t-tests and analysis of variance using R
• Conduct and interpret output from non-parametric analyses using R
This course is primarily assessed using individual midterm and final exams that are administered in person, which are each worth 50% of your final grade. Both the midterm and the final exam consist of multiple choice questions. Students need to pass both the midterm and the final exam to pass the course. In addition, students will be asked to submit 5 practical exercises. The exercises are not graded, but students will be expected to submit them and to provide a self-evaluation based on provided answers.
This course involves a combination of lecture, demonstration, and practical exercises designed to introduce students to the basics of both the statistical programming language R as well statistics. We will use RStudio, which makes it more efficient to work with R. We utilize R as it a powerful and scalable tool for working with and analyzing any type of data, and it’s free and open source.|
The topics in the course include the basics of the R statistical programming language and navigating the RStudio environment, creating different visualizations of data, understanding and utilizing functions that provide descriptive statistics about data, as well when and how to applying different inferential statistics including t-tests, analysis of variance, and non-parametric tests.