Statistics for Data Analyst: Percentile and Quartile

Before defining percentile and quartile , we should know why it is used for data analyst in stats; To remove outliers that can be helpful in analyzing data effectively.

Outliers –

· These are values in dataset which is completely differ and vary from others

· They are either larger values or significantly very small in dataset .

· They may affect analysis of data.

Lets take a example-

Dataset — {1,3,4,5,6,8,8,9,100}

Here 100 is outlier .

Percentile-

Percentile is a value below which a certain percentage of observation lie.

Lets understand it with the help of example in the given below dataset-

Dataset — {2,2,4,8,10}

  1. What is the percentile ranking of 10?

Here x is 4, n= 5

Therefore, Percentile rank of 10 = 4/5*100=80%

Means 80% of entire distribution is less than 10.

2. What value exist at percentile ranking of 25%?

= 25/100 *(5+1)

=1.5

Now here 1.5 is the index position in the given dataset.

As 1.5 will lie between 2 and 2, here we need to take average of it

And value will be 2

Quartiles-

· Values that divide the data into quarter.

· Used for finding interquartile range (IQR)

IQR is similar to range(max- min), whereas

IQR = Third Quartile-First Quartile

And yes IQR helps in removing outlier and to know how , you have to wait while for my next post which will be soon posted .Till then keep learning and keep growing.

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