The normal distribution curve is one of the most important statistical concepts in lean six sigma. Understand the role that probability distributions play in determining.
Normal Distribution
Typically in a six sigma project you are using dmaic methodology.
Six sigma probability distribution. Lets say during the define phase you chartered a project to see if you could improve a process that had a binomial outcome. 68 of the distribution area under the curve is about 1 standard deviation from the mean. For example you can easily look up the area under the standard normal curve greater than 124 in the table.
In the statistical tools of six sigma you frequently calculate probabilities using the standard normal table. Where e is a constant of 271828 x is the number of occurrences and l can equate to a sample size multiplied by the probability of occurrence ie npnpx 0 has application as a six sigma metric for yield which equates to y px 0 e l e du e dpu where d is defects u is unit and dpu is defects per unit. Understanding probabilities can provide black belts with the tools to make predictions about events or event combinations.
You even might use the binomial distribution to articulate the business case for why you should do the project in the first place for instance event a should be binomial but its clearly not. The normal distribution curve visualizes the variation in a dataset. Lean six sigma solves problems where the number of defects is too high.
The standard deviation is the square root of the variance and therefore 5. Probability of occurrence sigma value z cum of population 58 65 72 79 86 93 100 107 114 121 128 135 142003 135 2275 1587 500 841 977 9986 99997. This article will expand upon the notion of shape described by the distribution for both the population and sample.
Six sigma green belts receive training focused on shape center and spread. Therefore 35 5 30 is the lower value and 355 40 is the upper value. The probability from the table is 0107488.
Similarly there are two broad types of probability distribution depending on the data type of the random variable. In a six sigma context it is often important to calculate the likelihood that a combination of events or that an ordered combination of events will occur. The concept of shape however is limited to just the normal distribution for continuous data.
In my previous post on data type for lean six sigma projects opens in new tab we talked about two types of data. Types of probability distribution. Make assumptions given a known distribution.
A high number of defects statistically equals high variation in the process. Example of using binomial probability in a six sigma project.
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