About

Google Scholar

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Research

Research Interests

• Time Series Analysis
• Statistical Methods in fMRI
• Methods for High Dimensional Data
• Graphical Models
• Non-Parametric Statistics
• Extreme Value Theory
• Neuroeconomics

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Teaching

Teaching Interests

• Forecasting for Planners and Managers
• Applied Business Analysis of Time Series and Panel Data
• Multivariate Data Analysis
• Business Analytics

A Spotlight on Research at the Alberta School of Business

How is neuro statistics linked with decision-making?

My findings tell us...

• The brain's functionality and dynamic integration can be statistically analyzed by looking at networks.
• Statistical analysis of fMRI scans shows that people with gambling disorders have unique brain networks as they are making financial decisions.
• The comparison of networks can be further applied to a broad spectrum of brain disorders such as dyslexia, cerebral palsy and Alzheimer's. 

Read more about this research...

MGTSC 488 - Selected Topics in Management Science

Normally restricted to third- and fourth- year Business students. Prerequisites: MGTSC 312 or consent of Department. Additional prerequisites may be required.

Winter Term 2023 Winter Term 2023

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MGTSC 645 - Introduction to Business Analytics

The merging of massive data-sets with analytical tools from Statistics, Computer Science, and Operations Research has created the emerging field of analytics. Methods are developing rapidly based on statistical platforms such as SAS and R, or more general purpose programming tools such as Python. This course will build on the basis from MGTSC 501 to provide an overview of Big Data and analytics, and develop programming and methodological skills to acquire, analyze, and present analysis. Prerequisite: MGTSC 501.

Winter Term 2023

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MGTSC 705 - Multivariate Data Analysis I

An overview of multivariate data analysis normally taken by students in the first year of the Business PhD program. Designed to bring students to the point where they are comfortable with commonly used data analysis techniques available in most statistical software packages. Students are expected to complete exercises in data analysis and in solving proofs of the major results. Topics will include univariate analysis, bivariate analysis, multiple linear regression, and analysis of variance. It is expected that students have as background at least one semester of calculus, one semester of linear algebra, and two semesters introduction to probability, probability distributions and statistical inference. Prerequisite: Registration in Business PhD Program or written permission of instructor. Approval of the Business PhD Program Director is also required for non-PhD students.

Winter Term 2023

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Browse more courses taught by Ivor Cribben

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