# Continuous Distributions

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:

1) Uniform Distribution
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.

Find Six Sigma related job postings

Custom Search

Six Sigma

Templates, Tables & Calculators Six Sigma Slides

Green Belt Program (1,000+ Slides)

Basic Statistics

Cost of Quality

SPC

Process Mapping

Capability Studies

MSA

SIPOC

Cause & Effect Matrix

FMEA

Multivariate Analysis

Central Limit Theorem

Confidence Intervals

Hypothesis Testing

T Tests

1-Way ANOVA

Chi-Square

Correlation

Regression

Control Plan

Kaizen

MTBF and MTTR

Project Pitfalls

Error Proofing

Z Scores

OEE

Takt Time

Line Balancing

Yield Metrics

Sampling Methods

Data Classification

Practice Exam

... and more

## Need a Gantt Chart? 