z score
DESCRIPTION
standard distribution based scoring technique and implementationTRANSCRIPT
SMA BU Gading 2007 Prepared by [email protected]
Z-Score
Standard distribution based scoring technique and
implementation on scoring
SMA BU Gading 2007 Prepared by [email protected]
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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SMA BU Gading 2007 Prepared by [email protected]
That’s all
Thanks for your attentions
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