POSITION MEASUREMENTS

POSITION MEASUREMENTS

1. Measures of position not central

   • QUANTILE
     • DECILES
       • The deciles are the nine values ​​that divide the data series into ten equal parts. The deciles give values ​​corresponding to 10%, at 20%... and 90% of the data. D5 matches the median.
         • D9
         • D8
         • D7
         • D6
         • D5
         • D4
         • D3
         • D2
         • D1
     • PERCENTILE
       • Percentiles are the 99 values which divide the data series by 100 equal parts. The percentiles give the values ​​corresponding to 1%, to 2%... and 99% of the data. P50 matches the median.
     • QUARTILES
       • The quartiles are the three values ​​of the variable that divide an ordered data set into four equal parts. Q1, Q2 and Q3 determine the values ​​corresponding to 25%, 50% and 75% of the data. Q2 coincides with the median.
         • Q1
           • 25 OF THE DATA
         • Q2
           • 50% OF THE DATA
         • Q3
           • 75% OF THE DATA

2. Measures of position central

   • The arithmetic mean, geometric or harmonic
     • There are three central measures that indicate a weighted average of the data. The first It is the most used and best known of the three. The geometry is applied in series that show percentage growth. For his hand, the harmonica is useful in the analysis of stock market investments.
   • median
     • In this case, this is the measure of center position more recognizable. Split the distribution in two equal parts. Thus, it expresses the value medium, not medium. It is very useful in variables such as income or wages, while it is closely related to the average and some of the quantiles seen.
   • Fashion
     • We are facing a measure center of the values frequent. Therefore, the fashion tells us about those are repeated more often. This measure is very useful in market research when we measure an impression on a product with a scale likert.

3. The position measurements are statistical indicators that allow summarize the data into one, or divide your distribution into intervals the same size.