# Statistics - Descriptive Statistics

Descriptive statistics gives us insight into data without having to look at all of it in detail.

## Key Features to Describe about Data

Getting a quick overview of how the data is distributed is a important step in statistical methods.

We calculate key numerical values about the data that tells us about the distribution of the data. We also draw graphs showing visually how the data is distributed.

Key Features of Data:

- Where is the centre of the data? (location)
- How much does the data vary? (scale)
- What is the shape of the data? (shape)

These can be described by **summary statistics** (numerical values).

### The Centre of the Data

The **centre** of the data is where most of the values are concentrated.

Different kinds of averages, like mean, median and mode, are **measures** of the centre.

**Note:** Measures of the centre are also called **location parameters**, because they tell us something about where data is 'located' on a number line.

### The Variation of the Data

The **variation** of the data is how spread out the data are around the centre.

Statistics like standard devition, range and quartiles are **measures** of variation.

**Note:** Measures of variation are also called **scale parameters**.

### The Shape of the Data

The shape of the data can refer to the how the data are bunched up on either side of the centre.

Statistics like **skew** describe if the right of left side of the centre is bigger. Skew is one type of **shape parameters**.

### Frequency Tables

One typical of presenting data is with **frequency tables**.

A **frequency table** counts and orders data into a table. Typically, the data will need to be sorted into intervals.

Frequency tables are often the basis for making graphs to visually present the data.

## Visualizing Data

Different types of graphs are used for different kinds of data. For example:

- Pie charts for qualitative data
- Histograms for quantitative data
- Scatter plots for bivariate data

Graphs often have a close connection to numerical summary statistics.

For example, box plots show where the **quartiles** are.

Quartiles also tell us where the minimum and maximum values, range, interquartile range, and median are.