# 2.1: Introduction


##### Learning Objectives

By the end of this chapter, the student should be able to:

• Display data graphically and interpret graphs: stemplots, bar charts, frequency polygons, histograms, etc.

Once you have collected data, what will you do with it? Data can be described and presented in many different formats. For example, suppose you want to find a change in temperature in a particular city over time. Looking at all the raw data can be confusing and overwhelming. A better way to look at that data would be to create a graph that displays the data in a visual manner. Then patterns can more easily be discerned.

In this chapter, you will study graphical ways to describe and display your data. You will learn to create, and more importantly, interpret a variety of graph types, and you will learn when to use each type of graph.

A statistical graph is a tool that helps you 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. Then, more formal tools may be applied.

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 stem-and-leaf plots, line graphs, and bar graphs, as well as frequency polygons, and time series graphs.

This book contains instructions for constructing some graph types using Excel.