Distribution
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Mean Value: denoted by µ, is the likely average outcome from a random sampling process.
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Discrete System
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Continuous
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Variance: denoted by s2, indicates the spread of the distribution measured from the likely outcome m.
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Discrete System
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Continuous
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Standard Deviation: denoted by s, is the positive square root of the variance. It combines the spread of a distribution.
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Discrete System
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Continuous
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| Continuous Normal Distribution, also know as the Gaussian distribution, is the best-known and most widely used probability distribution. |
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Density Function
Distribution Function |
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Mean = µ
Variance = s2 Standard Deviation = s |
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Exponential Distribution, also known as the negative exponential, is useful in the calculations of reliability. The probability of the desired outcome diminishes as the trial number increases.
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| Density Function | Distribution Function |
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Mean = µ  Variance = s2  Standard Deviation = s |
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Discrete Binomial Distribution, also known as Bernoulli distribution. If the probability of occurrence of an event in each trial is p, and the probability of nonoccurrence is 1-p, then the probability of exactly x occurrence among n trial has the following properties:
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| Density Function | Distribution Function | ||
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Mean = µ
 Variance = s2  Standard Deviation = s  |
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Poisson Distribution is useful to describe the desired outcomes occur infrequently but at a regular rate. If the mean occurrence rate is V (the average of occurrence of the event) and the event took place during a time interval t, the poisson distribution with exactly x successes in the same sampling period has the following properties.
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| Density Function | Distribution Function |
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Mean = µ  Variance = s2 Standard Deviation = s  |
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