In any Six Sigma project, the team will have to take a sample of the population that they are studying. Lean Six Sigma courses cover a variety of sampling methods for this purpose. Sampling in statistics is usually applied in the Six Sigma Measure phase of the Six Sigma DMAIC cycle. Six Sigma Green Belt training […]

# Author: Leanie Louw

### Introduction to Collecting a Sample in Statistics

The Six Sigma approach to problem-solving is primarily data-driven. To obtain data in the Measure phase of the DMAIC process, it is important to understand sampling and the concept of a sample in statistics. A sample in statistics has very specific qualities. Lean Training Course and Green Belt training include a significant amount of information […]

### How to Calculate Probability Using the Poisson Distribution?

Lean Six Sigma Green Belt course graduates deal with two types of data during the Six Sigma Measure phase of their Six Sigma DMAIC projects: continuous data and discrete data. The Poisson distribution is a probability distribution for discrete data which takes on the values which are X = 0, 1, 2, 3 and so […]

### Why the Binomial Distribution is Useful for Six Sigma Projects

The Lean Six Sigma approach is fundamentally data-driven. It relies heavily on statistics to solve real-world problems, especially in the Measure phase and Analyze phases of the DMAIC process. One of the problems that practitioners who have successfully completed Lean Six Sigma Green Belt training face, is determining the probability of defectives resulting from a process. […]

### Understanding Discrete Probability Distribution

In the data driven Six Sigma approach, it is important to understand the concept of probability distributions. Probability distributions tell us how likely an event is bound to occur. Different types of data will have different types of distributions. Why do we need to know this? Well, in Lean Six Sigma Course we learn that […]