Monday and Tuesday October 7-8 Deeper Understanding of Standard Deviation Data Transformation

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Monday and Tuesday October 7-8 Deeper Understanding of Standard Deviation Data Transformation. Morbidity and Mortality Weekly Report. Life Expectancy. If you are male, your mean life expectancy at this time is 76. If you are female, your mean life expectancy is 82. Life Expectancy.
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Monday and TuesdayOctober 7-8Deeper Understanding of Standard DeviationData TransformationMorbidity and Mortality Weekly ReportLife Expectancy
  • If you are male, your mean life expectancy at this time is 76.
  • If you are female, your mean life expectancy is 82.
  • Life Expectancy
  • If you are male, your mean life expectancy at this time is 76.
  • If you are female, your mean life expectancy is 82.
  • Is this a small, medium, or big difference?
  • Life Expectancy
  • If you are male, your mean life expectancy at this time is 76.
  • If you are female, your mean life expectancy is 82.
  • Is this a small, medium, or big difference?
  • What is s=6? s=12? s=18?
  • Standard Deviation in Words
  • The standard deviation is an expression of the mean deviation of all the data points in the batch of data from the mean of the batch (expressed in the same unit of measurement as that for the mean)
  • _XCarla Roach’s question
  • If there is more variability in a sample, does that make its mean less meaningful?
  • Data TransformationLast week, we already saw one kind of data transformation:Percentile RankConverting scores to percentile ranks allows comparison across measures with different metrics. For example, you can ask if your percentile rank in height (inches) predicts your percentile rank in weight (pounds). Data TransformationLast week, we already saw one kind of data transformation:Percentile RankConverting scores to percentile ranks allows comparison across measures with different metrics. For example, you can ask if your percentile rank in height (inches) predicts your percentile rank in weight (pounds). Transforming interval scores to ordinal (percentile rank) scores lost information about the shape of the distribution.Data TransformationLast week, we already saw one kind of data transformation:Percentile RankConverting scores to percentile ranks allows comparison across measures with different metrics. For example, you can ask if your percentile rank in height (inches) predicts your percentile rank in weight (pounds). Transforming interval scores to ordinal (percentile rank) scores lost information about the shape of the distribution.The Z-score transformation converts scores to a standard format, with a mean of 0 and a standard deviation of 1, while preserving the shape of the distribution.Z-score transformation_Xi - XZi = Converts scores into the distance in standard deviation units from the mean, with negative values being below the mean and positive values being above the mean._Z = 0, z=1Because z-scores are in standard units:
  • you can compare positions across different variables that use different units of measurement (you can compare apples with oranges!)
  • you can quickly see if the position of an individual relative to the distribution is similar or different.
  • T-Score converts Z by multiplying by 10 and adding 50T = 10Z + 50This distribution has a mean of 50 and a standard deviation of 10.This conversion helps those who are frightened by negative numbers and decimal points.SATs and GREs are transformed to have a mean of 500 and a standard deviation of 100.SAT = 100Z + 500
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