(6 Credit Hours)
The PMST program focuses on modeling and statistical tools for solving real-life problems.
Students complete MST 6600 Applied Statistical Techniques (3 Credit Hours) and 3-credits of electives based on their program of study.
MST 6600 Applied Statistical Techniques
This case/example-based course introduces exploratory data analysis (EDA) using R. No prior knowledge of R is assumed. A primary objective is for students to apply graphical EDA techniques (including scatter plots, box plots, histograms, probability plots, residual plots, and control charts) to representative data sets using the RStudio platform. Additionally, students evaluate data using standard, quantitative statistical techniques. Practical exercises and industry cases are used throughout the term. This is a cohort class for first-year MST students. Permission of PMST Program Director required.
- Understand the differences between Exploratory Data Analysis (EDA) and classical, quantitative statistical analysis
- Apply graphical EDA techniques (including: scatter plots, box plots, histograms, probability plots, residual plots, and mean plots) to representative data sets using RStudio
- Evaluate data using classical, quantitative statistical techniques to establish the uncertainty of a point estimate
- Construct a hypothesis test to refute a specific claim about a population parameter based on the sample data
- Compile EDA and quantitative statistics into a report for communicating insight into data
- Present complex data in an organized and concise manner
- Manipulate complex data within the R/RStudio software environment as required for graphical and numerical analysis