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Z-Score

            Standard distribution based
              scoring technique and
            implementation on scoring

SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  1
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.
SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  2
Which the better one ?




SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  3
Standard probability




SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  4
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.

SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  5
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.

SMA BU Gading 2007    Prepared by febru354@yahoo.com
                                                   6
Z-Score Cont’d

                         Sample / small data / part of population




                              Total population / global




SMA BU Gading 2007    Prepared by febru354@yahoo.com
                                                   7
Z-Score Scenario

• Raw  Z-Score  Z- Std 




                                                          SUM
• Raw  Z-Score ========

           Global Expected Mean


•   One Parameter Only :
•   Expected upgrade >= 95% X (Max raw + Mean)
                                                     Matured Scores
SMA BU Gading 2007       Prepared by febru354@yahoo.com
                                                      8
Step 1 + 2




SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  9
Step 3 + 4




SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  10
Z-Score Effect
• Distribution is unchanged but its translated
  in order to be centered on the value 0.
• Proofed : SUM ( Z-Score ) = 0




SMA BU Gading 2007    Prepared by febru354@yahoo.com
                                                   11
SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  12
That’s all

             Thanks for your attentions
               febru@soluvas.com
                febru.soluvas.com

SMA BU Gading 2007   Prepared by febru354@yahoo.com
                                                  13

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Z score

  • 1. Z-Score Standard distribution based scoring technique and implementation on scoring SMA BU Gading 2007 Prepared by febru354@yahoo.com 1
  • 2. 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. SMA BU Gading 2007 Prepared by febru354@yahoo.com 2
  • 3. Which the better one ? SMA BU Gading 2007 Prepared by febru354@yahoo.com 3
  • 4. Standard probability SMA BU Gading 2007 Prepared by febru354@yahoo.com 4
  • 5. 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. SMA BU Gading 2007 Prepared by febru354@yahoo.com 5
  • 6. 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. SMA BU Gading 2007 Prepared by febru354@yahoo.com 6
  • 7. Z-Score Cont’d Sample / small data / part of population Total population / global SMA BU Gading 2007 Prepared by febru354@yahoo.com 7
  • 8. Z-Score Scenario • Raw  Z-Score  Z- Std  SUM • Raw  Z-Score ======== Global Expected Mean • One Parameter Only : • Expected upgrade >= 95% X (Max raw + Mean) Matured Scores SMA BU Gading 2007 Prepared by febru354@yahoo.com 8
  • 9. Step 1 + 2 SMA BU Gading 2007 Prepared by febru354@yahoo.com 9
  • 10. Step 3 + 4 SMA BU Gading 2007 Prepared by febru354@yahoo.com 10
  • 11. Z-Score Effect • Distribution is unchanged but its translated in order to be centered on the value 0. • Proofed : SUM ( Z-Score ) = 0 SMA BU Gading 2007 Prepared by febru354@yahoo.com 11
  • 12. SMA BU Gading 2007 Prepared by febru354@yahoo.com 12
  • 13. That’s all Thanks for your attentions febru@soluvas.com febru.soluvas.com SMA BU Gading 2007 Prepared by febru354@yahoo.com 13