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Non-Normal Probability Distributions, Other Types of Continuous…
Non-Normal Probability Distributions
Reviewing Normal Probability Distributions
Anatomy of a Normal Curve
Non-Normal Continuous Distributions
Lognormal Distribution: is used to when working with data sets that describe time durations, such as the time a process or machine is down, or a distribution of assets or wealth among the population.
Weibull Distributions: are related to continuous data.
Other Types of Continuous Distributions
Cauchy Distributions: do not have a defined mean or variance, which makes it not useful for many common statistical analysis tool in Six Sigma
Logistic Distribution is used in some science and math functions to approximate other symmetrical distributions
Laplace Distributions often referred to as the bilateral exponential distribution or the double-exponential distribution.
Uniform Distribution occurs when data points are divided evenly among bins.
Beta Distribution can take on a number of shapes and are considered extremely flexible.
Gamma distributions are similar to Beta and Weibull distributions. They are always skewed to the right.
Triangular Distribution is formed using the mode and the upper and lower limits of a data set.
Non-Normal Discrete Distributions
Binomial Distribution is used when you are dealing with discrete data and there are only two outcomes for each trial or sample.
Poisson Distribution if often used when dealing with data that is distributed randomly within time, distance, or other unit of measurement.
Other Types of Discrete Distributions
Geometric Distribution is used when there are two outcomes for a trial, trials are independent, and there is a waiting time before the first occurrence.
Negative Binomial is also used with attribute data - fail/pass and other situations where there are only two outcomes for each trial.
Applying Data to Real-World Situations
The normal distribution is related to continuous data.
Discrete data is not continuous in nature
The normal curve is symmetrical in nature.
Exponential Distribution: creates a histogram of trend line that is exponential in nature.