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.

Examples:

- The amount of time (in hours, minutes, and seconds) to run a race. A discrete option may be SLOW, FAST, FASTER, FASTEST.
- The height of people.
- The weight of a something in Kg. The discreate binomial option may be LIGHT or HEAVY
- The speed of a commuter train.

A BB/GB should be familiar with the following:

2) Normal Distribution

3) Exponential Distribution

4) T Distribution

5) Chi-Square Distribution

6) F Distribution

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.

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