Question

Twenty-six countries won medals in the 2006 Winter Olympics. The table lists them, along with the total number of medals each won: $$\text{Misplaced hline}$$ a) Try to make a display of these data. What problems do you encounter? b) Can you find a way to organize the data so that the graph is more successful?

Short answer

Use a bar graph ordered by medal count, or group data into medal ranges for better visualization.

Step-by-step solution

Understand the Data

The exercise provides a list of countries that won medals during the 2006 Winter Olympics along with the number of medals each country won. There are a total of 26 countries and varying medal counts.

Identify Problems with Data Display

A simple bar chart or list may be difficult to read due to the large number of countries (26 entries) and varying medal totals. There is a need for efficient categorization and visualization techniques to meaningfully display this data.

Suggest an Effective Display Method

A horizontal bar graph could be used, arranged in descending order of medals won. This allows for easier comparison among countries. Additionally, grouping countries by the number of medals into ranges (e.g., 1-5, 6-10, etc.) can make the graph more concise.

Develop an Alternative Solution

Consider using a histogram where countries are grouped into ranges based on the number of medals won, or use a pie chart highlighting countries within specific medal range categories, which can help visualize the proportion of countries winning certain medal amounts.

Key concepts

Olympic medal counts

The Olympic Games is one of the world's most celebrated sporting events where countries from all over the globe compete for medals. Each country aims to perform at their best, and their total medal count is often seen as a measure of their success. In the 2006 Winter Olympics, 26 countries won medals, which made the medal count quite diverse.

Understanding these medal counts involves recognizing both the number of medals won and the spread across different countries. This diversity makes it important to find effective ways to visualize and comprehend these numbers. Medal counts are not just numbers; they represent the hard work and competitive spirit of each country's athletes.

horizontal bar graph

A horizontal bar graph is an excellent tool for visualizing data like Olympic medal counts where there are many categories. In this context, each bar represents a country, and the length of the bar is proportional to the number of medals won.

Why a horizontal bar graph? This format is particularly effective when dealing with lengthy category names, such as country names, because it provides ample horizontal space to display them clearly. By arranging the bars in descending order, it becomes easier to compare the medal achievements of different countries at a glance. A horizontal orientation helps in accommodating long labels without issue, making the graph easy to read and interpret.

grouping data

Data grouping is a method used to make complex datasets easier to understand. In the case of Olympic medal counts, instead of listing each country individually, countries can be grouped into categories based on the number of medals won.

For example, you could group countries that won 1-5 medals together, 6-10 medals together, and so on. This method simplifies the dataset and highlights trends, by allowing us to see how many countries fall into each group quickly. Grouping helps in reducing cognitive load, making it an effective way to convey information concisely and clearly in a visualization.

histogram

A histogram is another effective way to visualize the Olympic medal data. Unlike a bar graph, which presents each country individually, a histogram groups data into bins or ranges. Each bin represents a range of medal counts and shows how many countries fall into each range.

For instance, a bin could represent countries with 1-5 medals, another for 6-10 medals, and so forth. This visualization allows you to understand the distribution of medal counts across all countries. It is particularly useful for identifying clusters or sets within the data, providing insights into which ranges of medal counts are most common among the countries."