Measures of Distribution Shape,
      Relative Location, and Detecting Outliers
      Distribution Shape
      Chebyshev’s Theorem
      Empirical Rule
      Detecting Outliers
                                                       
                                                       
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                Distribution Shape
      In order to understand the shape of a 
       distribution, we will refer to Histogram 
       discussed in Chapter 2.
      By looking at the Histogram, we will determine 
       the shape of the distribution.  
      The shape of a distribution is measured with a 
       quantity called Skewness.
                                                     
                                                     
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           Distribution Shape:  Skewness
      An important measure of the shape of a 
       distribution is called skewness.
      The formula for computing skewness for a data 
       set is somewhat complex.
      Skewness can be easily computed using 
       statistical software.
                                                     
                                                     
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           Distribution Shape:  Skewness
      Symmetric (not skewed)
       • Skewness is zero.
       • Mean and median are equal.
             .35      Skewness = 
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           Distribution Shape:  Skewness
      Moderately Skewed Left
       • Skewness is negative.
       • Mean will usually be less than the median.
             .35   Skewness  =  - .31  
            y.35
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            c.30
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              0
                                                     
                                                     
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