UNIVARIATE STATISTICAL MEASURES

UNIVARIATE STATISTICAL MEASURES

1. MEASURES OF DISPERSION

   • Distance in between data
     • Absolute
       • Standard Deviation
         • The standard deviation or typical deviation is a measure that provides information about the average dispersion of a variable. The standard deviation is always greater than or equal to zero.
       • Variance
         • Variance is a measure of dispersion that represents the variability of a series of data with respect to to your average. Formally it is calculated as the sum of the squared residuals divided by the total of observations. It can also be calculated as the squared standard deviation.
       • Range Walk
         • The range, amplitude or range, is used to get a quick idea of ​​​​the degree of dispersion or separation of a data set
     • Relative
       • Frequency distribution comparison
         • They are indicators of the dispersion of the distribution that have been relativized, so that they do not affect the units of measurement of the variable and so that comparisons can be made between the dispersions of disparate data sets.
           • COEFFICIENT OF VARIATION
             • WHAT IS IT
               • The coefficient of variation, also known as Pearson's coefficient of variation, is a statistical measure that tells us about the relative dispersion of a data set. In other words, it informs us, like other measures of dispersion, of whether one variable moves a lot, a little, more or less than another.
             • HOW THE COEFFICIENT OF VARIATION IS CALCULATED
               • CV = standard deviation / arithmetic mean x 100 This coefficient is used to compare data sets from different populations, taking into account the value of the arithmetic mean, which allows us to eliminate possible distortions in the means of two or more populations.
   • TYPED VARIABLE
     • This new variable lacks units and makes it possible to compare two measures that initially are not, because they allude to different concepts.
   • Chebyshev's inequality is a theorem used in statistics that provides a conservative estimate (confidence interval) of the probability that a random variable with finite variance lies within a certain distance from its mathematical expectation or its mean.

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