# Understand The Data using Simple Measures

In our daily lives, we collect or deal with different kinds of data. Almost, the data exists in any business nowadays and it presents the assets of our business, for example, in the health industry, you may need to know the data about the number appointment, the number of the doctors per specialty, the staff’ performance, the bed occupations, the mortality rate, the busiest day, the revenues, …etc.

In this article, we will define the types of data we deal with every day, and what are the measures of center and spread for data, which are powerful measurement tools to understand the data in a simple, better, and an easy way.

Absolutely, there is a dozen available ready-made tools and applications to calculate these measures easily, while I believe it is important to know how to calculate, use them and why.

## What are the types of data?

Any data can be categorized in two categories; Quantitative and Categorical.

### Quantitative Data

Quantitative data has numeric meaning and used in calculations, for example, number of employees, number of sales, …etc. Quantitative data has two categories either continuous or discrete.

Continuous data mainly is infinite, measurable, can be broken into smaller units, for example, age, weight, or height, …etc.

Discrete data mainly is finite, countable, and cannot be broken into smaller units, for example, number of customers, number of employees, …etc.

### Categorical Data

A categorical data describes a quality or characteristic of something without numerical or quantitative values, for example, Movies rating, Education Level, Nationalities, …etc. Similarly, categorical data has two categories either ordinal or nominal.

Ordinal data mainly is ordered, for example, ratings of movies, education levels, age levels, …etc.

Nominal data is the kind of data which cannot be ordered and the order will not provide any meaningful value, so the values are just labels, for example, countries, gender, colors, …etc.

Furthermore, the below table summarizes different examples of the types of data

 Quantitative Data Continuous Discrete Age – weight – height – Blood Pressure – Speed Number of customers – Number of employees – Days in a week – Days in a year Categorical Data Ordinal Nominal Movies ratings – Education levels – Age levels – Height levels – Agreement levels – Satisfactions Levels Countries – Gender – Colors – Blood groups – Industry Group

Types of data examples

## Measures of Data Center

The measures of the center are mainly used with the quantitative data only because they are numeric values as we explained before. We have 3 measures of center. And it is valuable to understand our data.

Meanwhile, to understand that, let us assume that we have the number of sales per month as per the graph below.

We have this value according to the months:

[Jan = 5, Feb = 6, Mar = 10, Apr = 11, May = 1, Jun = 2, Jul = 5, Aug = 10, Sep = 5]

The number of values here, which is equal to the dataset size is = 9

### Mean

Mean is the average value of the data. Also, it is known as “arithmetic mean,” it is calculated by adding all the data values in a set and dividing this summation value by the number of the values in the dataset.

#### Example:

Using the same example, we may be interested to know the average of the sales across all months so you can simply do that by using the values we plotted here.

[5, 6, 10, 11, 1, 2, 5, 10, 5]

Number of values = 9

Mean = 5 + 6 + 10 + 11 + 1 + 2 + 5 + 10 + 5 / 9 = 6.11

### Mode

The mode is the number which appears most often in a set of numbers. And it is not commonly used in statistical calculations.