Six Sigma Demystified® by Paul Keller

Author:Paul Keller
Language: eng
Format: mobi
ISBN: 9780071760829
Publisher: McGraw-Hill
Published: 0101-01-01T00:00:00+00:00

Distributions

When process and population data are fit by assumed distributions, broad predictions can be made with minimal data. Popular statistical distributions include the binomial and Poisson distributions for discrete (count) data and the normal, exponential, and robust Johnson and Pearson distributions for continuous (measurement) data.

When to Use

Measure to Control Stages

• To estimate properties of an existing or potential process or population, including its failure rate or sigma level

• To generate random data for process modeling

Binomial Distribution

The binomial distribution is used to estimate the number of units meeting one of two possible conditions in a process or population. For example, if the population is the total number of orders shipped in July, the condition of interest might be the number of units shipped on time. Since there is only one other possible condition (the order is not shipped on time), the binomial distribution is appropriate for modeling the number of units shipped on time. It may be applied when the number of samples is fixed and trials are independent with equal probability of success.

Binomial Distributions

Minitab

Use Calc\Random Data\Binomial to generate random numbers using a fixed sample size and p value.

Excel

Use Data\Data Analysis\Random Number Generation.

Set Distribution = Binomial using a fixed sample size and p value.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.