If the data is classified as Continuous, the following are links to most common Continuous Distributions used in Six Sigma projects.
A continuous probability distribution is one in which a continuous random variable X can take on any value within a given range of values. The amount of values can be infinite.
For example, measuring the temperate in degrees can be take on an infinite number of values depending on how many decimals you allow. However if the scale is COLD, WARM, and HOT then the data is Discrete. You can see how continuous data can provide more information than discrete data.
A BB/GB should be familiar with the following:
A few other continuous distributions are the Beta, Erlang, Gamma, Cauchy, Lognormal, Weibull, Double Exponential, Power Normal, Power Log Normal, Bivariate Normal, Logistic, Gumbel, and Tukey-Lambda.
Click here for an overview of Discrete Distributions.
Six Sigma Slides
Green Belt Program (1,000+ Slides)
Cost of Quality
Cause & Effect Matrix
Central Limit Theorem
MTBF and MTTR
... and more