Data collection generally consists of gathering information over many variables. It is imperative that we can summarize this data in a way that is quick and easy to understand. This will help us explore and understand the data and assist in making decisions through evidence. There are many ways to visualize data: through tables, graphs, and animations. Which data visualization should be used will depend on the type of data to be summarized. The ease of implementation is certainly an attractive attribute of data visualization techniques, but it can also lead to misuse. Visual techniques may be implemented in a way that it will not help summarize the data, mislead about what the data is showing, or flat out provide erroneous output. Just because a software program provides you with a graph, it does not mean that the graph is correct. The person preparing the visual display must provide the right type of data to the computer program. In this chapter, we will introduce the use of visualization tools and expand on these tools throughout the book.
3.2 Visualization Methods for Categorical Variables
As indicated in Chapter 2, data is classified as categorical (qualitative) or numerical (quantitative). A numerical variable either counts or measures something, while a categorical variable groups a trait of a data set based on classifications. Usually, data sets include both types of variables. With categorical variables, the least amount of mathematical ...