Course of Statistics (Intermediate Level): Visualisation of Numerical Data [06]

Hello! Welcome, let's start the sixth lesson on statistics. Today, we will discuss “How to visualize Numerical Data?”. Once again, at the end of the class, we will have practical examples in Excel and Python.
In previous lectures, we have learned the difference between categorical and numerical data.

Remember, numerical data always involves some kind of measurement, or it is a quantity that can be counted.

For example, the weight of cats that have visited a pet store in one day.

To obtain these data, you need to weigh the cats on a scale. You need to measure them.

In turn, to quantify the number of romance books in each apartment of a given flat, you need to count the number of such books for each apartment.

Categorical data is non-numeric information that can be expressed as a list of words.

Today, we are going to talk about numerical data visualization.

Numerical data visualization presents numerical data in the form of charts, graphs, and other visual aids.

These are useful ways of better exploring, understanding, and communicating data.

The goal here is to summarize and aggregate information to show the relationships and patterns found in the data better.

A good definition of numerical data visualization is that it involves the use of visual representations to explore, make sense of, and communicate data.

We need to explore the data in our search for significant facts.
We also need to make sense of the data, to gain a deeper and holistic understanding of it.

Visualization reveals the big picture, an overview of a data set.

Finally, if we can better understand data in a visual form, so can others. It is then useful to use visual aids to convey information to others.

The first thing we can do, especially for numerical data, is to examine the data in a frequency table. After that, we can apply a visual tool to the data.

We have many graphical options for numerical data.

Some of them we will discuss here, such as stem plots, dot plots, line graphs, histograms, frequency polygons, and time series plots.

A stemplot is represented as a special table where each data observation or value is split into a "stem", which is the first digit or digits, and a "leaf," which is usually the last digit.

Another type of graph is the line graph.

This graph is useful for showing trends in discrete ungrouped data from small datasets.

Dot plots are very similar in functionality to stem plots, but they are simpler to read. A dot plot consists of dots, or points, where each dot represents only one data point. These plots are useful to help find an overall pattern.

In many of the cases in statistics, histograms are used to display numerical data. One advantage of a histogram is that it can readily display large continuous data sets. It is applicable only to grouped numerical data, either discrete or continuous.

Frequency polygons are analogous to line graphs but utilize binning techniques to make grouped data visually easy to interpret. It is fundamentally a combination of a histogram and a line graph.

Thank you for watching, and see you soon!