On the D-day of taking GRE, you will have two quantitative aptitude sections and if luck forbids then a third quant experimental section will also grace your paper. So, one thing is confirmed that you will be getting DI questions in your final GRE. In fact, if you get one graph you will be getting a minimum of three questions based on those charts. So overall in two sections you will get at least six DI questions and if an experimental section is included, then minimum nine questions. Hence, no doubt data interpretation or DI is considered to be a major topic in GRE. So, if you are not a math geek, then it’s high time that you start reading newspapers and business magazines to understand the story these graphs portray.

## Why Data Interpretations?

The biggest question to bug your mind is why DI is even included in the syllabus of GRE? Why consider data interpretation an important topic?

All these answers lie in a single statement. There’s a famous proverb, “a picture is worth a million words,” and same applies to graphical and tabular representation. A single picture can give a lot of information and is the best way of expressing various business data in the least amount of space. No wonder many grads and B-schools give such high importance to data interpretation.

Topics you need to pay attention in data interpretation:

Data interpretation includes different topics, and it is important for a GRE aspirant to be familiar with each topic. These topics are:

- Bar charts
- Pie charts
- Line graphs
- Histograms
- Best fit lines
- Scatterplots
- Boxplots
- Maps of numerical data

## Random Variables

A random variable in statistics is a quantity whose possible values depend, in a random manner, on a set of random outcomes events. A random variable is defined as a function that maps probability to a physical outcome (labels), typically real numbers.