For the random sample of Groupon purchases by the university...

For the random sample of Groupon purchases by the university students in Exercise 1.57, the following breakdown by gender was obtained: $$ \begin{array}{lccc} \hline & \text { Male } & \text { Female } & \text { Total } \\ \hline \text { Activities } & 140 & 90 & 230 \\ \text { Food } & 45 & 35 & 80 \\ \text { Health related } & 20 & 70 & 90 \\ \text { Other } & 10 & 40 & 50 \\ \text { Total } & 215 & 235 & 450 \\ \hline \end{array} $$ a. Graphically depict the type of purchase by gender with a component bar chart. b. Graphically depict the type of purchase by gender with a cluster bar chart.

For the random sample of Groupon purchases by the university students in Exercise 1.57, the following breakdown by gender was obtained:

$$
\begin{array}{lccc}
\hline & \text { Male } & \text { Female } & \text { Total } \\
\hline \text { Activities } & 140 & 90 & 230 \\
\text { Food } & 45 & 35 & 80 \\
\text { Health related } & 20 & 70 & 90 \\
\text { Other } & 10 & 40 & 50 \\
\text { Total } & 215 & 235 & 450 \\
\hline
\end{array}
$$
a. Graphically depict the type of purchase by gender with a component bar chart.
b. Graphically depict the type of purchase by gender with a cluster bar chart.

Key Concepts

Component (Stacked) Bar Chart

A component or stacked bar chart is a graphical representation that divides each bar into segments proportional to the sub-categories that make up the total. It allows for the visualization of the cumulative contribution of each sub-category (in this case, different types of purchases) within a primary category (gender), making it easier to see the overall composition at a glance.

Cluster Bar Chart

A cluster bar chart, also known as a grouped bar chart, displays bars for each sub-category side by side within each primary category. This format facilitates direct comparison across different sub-categories (purchase types) for each main category (gender) by positioning corresponding bars close together, highlighting differences in counts between groups.

Categorical Data Analysis

This concept involves the examination and interpretation of data that can be categorized into distinct groups. In the context of the problem, different types of purchases and gender classifications are examples of categorical data. Analyzing such data typically involves creating tables and graphs that summarize the frequency or count of observations in each category.

Contingency Table

A contingency table, or cross-tabulation, is a matrix format that displays the frequency distribution of categorical variables. In this scenario, the table shows the counts for different purchase types by gender, which is essential for performing comparisons and visualizing the relationship between these two categorical variables.

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