Chapter 2: Organizing Data

Once a sample is collected, we can organize and present the data in tables and graphs. These tables and graphs help summarize, interpret and recognize characteristics within the data more easily than raw data. There are many types of graphical summaries. We will concentrate mostly on the ones that we can use technology to create.

A population is a collection of all the measurements from the individuals of interest. Remember, in most cases you cannot collect data on the entire population, so you have to take a sample. Now you have a large number of data values. What can you do with them? Just looking at a large set of numbers does not answer our questions. If we organize the data into a table or graph, we can see patterns in the data. Ultimately, though, you want to be able to use that table or graph to interpret the data, to describe the distribution of the data set, explore different characteristics of the data and make inferences about the original population.

Some characteristics to look for in tables and graphs:

1. Center: middle of the data set, also known as the average.
2. Variation: how spread out is the data.
3. Distribution: shape of the data.
4. Outliers: data values that are far from the majority of the data.
5. Time: changing characteristics of the data over time.

There is technology that will create most of the graphs you need, though it is important for you to understand the basics of how they are created.

Qualitative data are words describing a characteristic of the individual. Qualitative data is graphed using several different types of graphs, bar graphs, Pareto charts, and pie charts. Quantitative data are numbers that we count or measure. Quantitative data graphed using stem-and-leaf plots, dotplots, histograms, ogives, and time series.