Example Syllabus from Winter 2024
Example Topics Schedule from Winter 2024
Have you had an introductory course in statistics? Are you interested in diving deeper into statistical methods that aid in decision-making? Then this is the course for you. In MA487, we use data collected from controlled experiments to draw causal conclusions and identify important factors to processes. Students who take MA487 have the opportunity to
This is not a class where you will quietly sit and take notes. Instead, you should come prepared to work with classmates on a regular basis, discuss ideas during lecture, and actively participate during the study design sessions.
We focus heavily on the study design aspect, with much less attention to the statistical theory. (If we wanted to do a deep dive into the theory for this material, everyone would need to have previous coursework in probability and mathematical statistics.) The applied approach to this material allows us to learn about advanced experimental design techniques. This translates to the course also being a great prep for students interested in graduate studies in statistics.
Overseeing processes and improving upon currently utilized methods are two tasks many engineers and scientists face during their career. There is pressure to simultaneously increase profit margins and maintain high standards of product integrity. To improve upon current processes, scientists are asked to search for alternative methods or materials than those typically used. To increase profits in our rapidly changing world, innovation is key, and engineers and scientistis are drivers to creating new products. Moreover, once a product is deemed fit for the consumer market, engineers oversee the processes that ensure quality.
A natural intersection of engineering, science, and statistics is through design of experiments. The statistical tools that comprise the design of experiments subject area are integral to the informed decision-making process that engineers utilize in ensuring quality, improving product, and innovating processes. These tools allow scientists to collect data that provides insights to process components that are causing improvement or deterioration in product. Analysis of data collected from a designed experiment may lead to discovery of practical process settings that are cost-efficient for the company. Furthermore, a well-designed experiment has possibility of leading to a groundbreaking discovery.
Individuals with strong statistical analysis skills are in high-demand. Statistician and Data Scientist positions top multiple desirable job lists online, and receive lucrative pay
Essentially anyone working in STEM needs the ability to analyze data. This course gives you more advanced knowledge for performing your own data analyses.
You will be able to recommend appropriate designs/statistical models based upon a question of interest. By carefully selecting the model and controlling for sources which contribute to variation in processes, the collected data may be used to identify factors which effect a quantitative variable of interest as well as describe the relationships between multiple factors and the response. Topics we cover in this course include