Mohua Podder, PhD and MSTAT (Statistics), MA (Economics)

Area of Study / Keywords

Econometric Models Robust Classification Dynamic variable selection

About

Education/Certification

• MA in Economics, University of Alberta, Edmonton, AB (2017)
• PSAT accreditation from Statistical Society of Canada (2014)
• Ph.D. in Statistics, University of British Columbia, Vancouver, BC (2008)
• Masters of Statistics (MSTAT), Indian Statistical Institute, Kolkata, India (2003)

Research

My research focuses on dynamic variable selection in econometric models, including hierarchical and non-nested frameworks, as well as the application of robust methodologies in classification models. I aim to develop statistical methods that improve model accuracy and stability in economic and business settings.

Teaching

I started teaching in 2015, and since then, I have taught courses in business analytics, decision sciences, and quantitative methods, helping students connect theory to real-world applications. I aim to simplify complex concepts and engage students while encouraging critical thinking and problem-solving. Beyond the classroom, I support student success by organizing help sessions, managing teaching teams, and mentoring students. My goal is to ensure learning is practical, accessible, and impactful.

MGTSC 212 - Probability and Statistics for Business

This course deals with model building, multiple regression analysis, and related methods useful in a business environment. Microcomputer software will be utilized throughout the course, with necessary computing skills being taught as the course proceeds. However, students are expected to already possess some basic familiarity with microcomputer applications. Prerequisite: STAT 161 or equivalent. Credit will be granted for only one of MGTSC 212 (formerly MGTSC 312) and STAT 252. Students may not receive credit for both MGTSC 212 and MGTSC 312.

MGTSC 405 - Forecasting for Planners and Managers

This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352.

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