# 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}

**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 outlierand to know how , you have to wait while for my next post which will be soon posted .Till thenkeep learning and keep growing.