Strategies for Analyzing Count DataMarch 19, 2025 •3:00 PM UTC
Count data is one of the most common forms of data in continuous improvement—whether you're counting defects, clicks, complaints, or events. But not all count data is created equal. Datasets that have infrequent data over time, overdispersion, or display data across multiple categories or departments each require tailored analytical techniques to draw reliable conclusions.
For example, a company tracking infrequent safety incidents may struggle to detect meaningful trends, while an organization receiving thousands of customer complaints needs to uncover patterns in large datasets. In both cases, traditional statistical methods often fail to provide reliable answers.
Join us for this educational webinar as we explore how to analyze count data based on its size and frequency. Using real-world examples, we’ll show you how to apply statistical tests like the Poisson Goodness of Fit Test and the 1-Sample Poisson Rate Test. You’ll leave with a solid understanding of when and how to use these techniques, along with actionable insights for your process improvement work.
What You'll Learn
- Count Data 101: Why count data behaves differently and how to recognize its unique challenges.
- Techniques for Small Count Data: When to use the Poisson Goodness of Fit Test and 1-Sample Poisson Rate Test.
- Analyzing Large and Frequent Data: Assumptions that can be made for larger datasets.
Who Should Attend
- Continuous Improvement Professionals
- Lean Six Sigma Practitioners
- Quality Engineers and Managers
- Analysts working with count-based metrics in operations
- People who count things
Don’t miss this opportunity to make better decisions analyzing your count data!
Master Black Belt • MoreSteam Client Services
Kevin Keller is a professional statistician and quality professional with over 30 years of experience teaching, coaching, and leading Lean Six Sigma project teams from the shop floor to the enterprise levels. He began his career as a process engineer at Texas Instruments and then served in a Master Black Belt role at MEMC Electronic Materials for 15 years. Kevin advanced to manage quality systems for AB‐InBev and eventually accepted a Master Black Belt position for AB‐InBev's North American Zone. Kevin earned a BS in Chemical Engineering from Missouri University of Science and Technology and a Masters in Applied Statistics from The Ohio State University. He also is a certified Lean Six Sigma MBB.
