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 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 |
|---|---|
| Practice exercises | 10% |
| Assignments | 45% |
| Midterm 1 | 15% |
| Midterm 2 | 15% |
| Final Project | 15% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
- The Statistical Sleuth: A Course in Methods of Data Analysis, 3rd edition, by Ramsey and Schafer. (https://www.cengage.com/c/the-statistical-sleuth-3e-ramsey-schafer/9781133490678/)
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.