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Z-Score

Standard distribution based scoring technique and

implementation on scoring

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Standard Deviation• The standard deviation is the most

common measure of statistical dispersion, measuring how widely spread the values in a data set are. – If many data points are close to the mean,

then the standard deviation is small; – if many data points are far from the mean,

then the standard deviation is large. – If all the data values are equal, then the

standard deviation is zero.

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Which the better one ?

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Standard probability

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Z-Score• In statistics, the standard score, also called the z-score or

normal score, is a dimensionless quantity derived by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing.

• The standard score indicates how many standard deviations an observation is above or below the mean. It allows comparison of observations from different normal distributions, which is done frequently in research.

• The standard score is not the same as the z-factor used in the analysis of high-throughput screening data, but is sometimes confused with it.

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Z-Score Cont’d

• The quantity z represents the distance between the raw score and the population mean in units of the standard deviation. z is negative when the raw score is below the mean, positive when above.

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Z-Score Cont’d

Sample / small data / part of population

Total population / global

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SU

M• Raw Z-Score Z- Std

• Raw Z-Score ========

• One Parameter Only :• Expected upgrade >= 95% X (Max raw + Mean)

Z-Score Scenario

Global Expected Mean

Matured Scores

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Step 1 + 2

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Step 3 + 4

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Z-Score Effect • Distribution is unchanged but its translated

in order to be centered on the value 0.

• Proofed : SUM ( Z-Score ) = 0

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That’s all

Thanks for your attentions

febru@soluvas.com

febru.soluvas.com