Course Description
Introduction to fundamental statistical techniques for data analytics such as hypothesis testing, data transformation, estimation, confidence intervals, regressions models, ANOVA, multivariate analysis, non-parametric methods, and design of experiments. Prerequisite: Student in the M.S. in Informatics and Analytics or the M.S. in Applied Statistics program or permission of instructor.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
Identify a statistical technique appropriate to address a given research question
Analyze the implications of study design on the type of statistical inference
Demonstrate the implications of violations of assumptions of statistical methods, and identify adjustments or alternative procedures when necessary
Apply various data analysis tools for one, two and multi-sample inference
Reproduce Model selection techniques for various analyses
Communicate clearly and correctly the results of statistical analyses
Course Grading Information:
Activity/Performance Measure | Percentage/Points |
|---|---|
Assignment 1: Exercises completed by teams with participation in group activities | 10% |
Assignment 2: Final report due first day of final examinations (late penalty applies) | 80% |
Assignment 3: Oral presentation summarizing the team’s final report | 10% |
Participation/Professionalism: Team-based grading with misconduct policy | 0% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course, unless there is an issue with misconduct per policy.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.