2.1 Introduction to Descriptive Statistics

Once we have collected data, what do we do with it?  Data can be described and presented in many different formats.  For example, suppose you are interested in buying a house in a particular area.  You may have no clue about the house prices, so you might ask your real estate agent to give you a sample data set of prices.  Looking at all the prices in the sample is often overwhelming.  A better way might be to look at the median price and the variation of prices.  The median and variation are just two ways that we can summarize and describe data.  Your agent might also provide you with a graph of the data.

In this chapter, we will study numerical and graphical ways to describe and display your data.  This area of statistics is called descriptive statistics.  We will learn how to calculate, and more importantly, how to interpret these measurements and graphs.

A statistical graph is a tool that helps us learn about the shape or distribution of a sample or a population.  A graph can be a more effective way of presenting data than a mass of numbers because we can see where data clusters and where there are only a few data values.  Newspapers and the internet use graphs to show trends and to enable readers to compare facts and figures quickly.  Statisticians often graph data first to get a picture of the data, and then use more formal tools to analyze the data.

Some of the types of graphs that are used to summarize and organize data are the dot plot, the bar graph, the histogram, the stem-and-leaf plot, the frequency polygon (a type of broken line graph), the pie chart, and the box plot.  In this chapter, we will briefly look at bar graphs (or histograms), as well as frequency polygons and time-series graphs.

Attribution

“Chapter 2 Introduction” in Introductory Statistics by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.