Before you apply statistical techniques to a dataset, it's important to examine the data to understand its basic properties. You can use a series of techniques that are collectively known as *Exploratory Data Analysis* (EDA) to analyze a dataset. EDA helps ensure that you choose the correct statistical techniques to analyze and forecast the data. The two basic types of EDA techniques are *graphical* techniques and *quantitative* techniques.

## Graphical EDA techniques

Graphical EDA techniques show the key properties of a dataset in a convenient format. It's often easier to understand the properties of a variable and the relationships between variables by looking at graphs rather than looking at the raw data. You can use several graphical techniques, depending on the type of data being analyzed. You use the following:

Box plots

Histograms

Normal probability plots

Scatter plots

## Quantitative EDA techniques

Quantitative EDA techniques provide a more rigorous method of determining the key properties of a dataset. Two of the most important of these techniques are

Interval estimation.

Hypothesis testing.

*Interval* estimates are used to create a *range* of values within which a variable is likely to fall. *Hypothesis* testing is used to test various propositions about a dataset, such as

The mean value of the dataset.

The standard deviation of the dataset.

The probability distribution the dataset follows.

Hypothesis testing is a core technique in statistics.