Carly Metcalfe Headshot

Carly Metcalfe

Senior Lecturer, Applied Statistics

School of Mathematics and Statistics
College of Science
Undergraduate Program Coordinator, Applied Statistics

585-475-6529
Office Hours
FALL 2025: M/W 1:00pm - 2:00pm (office), T/TH 3:30pm - 4:30pm (zoom)
Office Location

Carly Metcalfe

Senior Lecturer, Applied Statistics

School of Mathematics and Statistics
College of Science
Undergraduate Program Coordinator, Applied Statistics

585-475-6529

Currently Teaching

STAT-205
3 Credits
This course covers basic statistical concepts and techniques including descriptive statistics, probability, inference, and quality control. The statistical package Minitab will be used to reinforce these techniques. The focus of this course is on statistical applications and quality improvement in engineering. This course is intended for engineering programs and has a calculus prerequisite. Note: This course may not be taken for credit if credit is to be earned in STAT-145 or STAT-155 or MATH 252..
STAT-495
1-3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
STAT-498
1-3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
STAT-521
3 Credits
This course presents the probability models associated with control charts, control charts for continuous and discrete data, interpretation of control charts, and some standard sampling plans as applied to quality control. A statistical software package will be used for data analysis.
STAT-621
3 Credits
A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques.
STAT-641
3 Credits
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.