**Introduction to five number summary**

Given a dataset, five number summary provides an insight on distribution of data. It highlights the quartiles and percentiles in data.

Before moving with the calculation of five number summary it is recommended that you sort the dataset in ascending order (This is not a mandatory step, but it helps a lot for manual calculations)

**Five number summary**

It constitutes the following elements

- Minimum value – The lowest value in the dataset
- Q1 (First Quartile) – The value below which 25% of the data falls
- Q2 (Median) – The value below which 50% of the data falls
- Q3 (Third quartile) – The value below which 75% of the data falls
- Maximum Value – Highest value in the dataset

From percentiles perspective, Q1, Median and Q3 correspond to 25^{th} percentile, 50^{th} percentile and 75^{th} percentile respectively

**Example**

Consider the dataset given below.

Five number summary for the below dataset is presented below

## Box whisker Plot

Box plot shows the five number summary graphically (Shown below is the five number summary for the dataset considered above)

**How to identify Outliers in data?**

Outliers are those values in the data that deviate far off from the rest of the values (Something like odd man out).

Outliers could be on higher side or on the lower side. It is important to identify outliers as they can heavily influence the outcome of analyses performed on the data

**Identifying outliers**: Now that we know that outliers deviate far off from other values in the dataset, the next task is to understand what level of deviation makes a datapoint an outlier?

1.5 times IQR rule: This is the most commonly used method to identify the outliers

IQR refers to the interquartile range and is given by

Task:

Identify outliers in below data using 1.5*IQR rule

Data: {4, 7, 8, 1, 2, 3, 9, 10, 10, 11, 12, 13, 13, 14, 15, 22}