A scatter plot is the most suitable visualization for examining the relationship or correlation between two continuous numerical variables. In this scenario, 'age' and 'income' are both continuous variables. Each employee can be represented as a point on the graph, with their age plotted on the x-axis and their income on the y-axis. This visual representation allows the human resources department to easily identify patterns, trends (e.g., does income increase with age?), and outliers within the dataset.

B. Area plot: An area plot is typically used to show how a quantitative value changes over a continuous dimension, such as time, and is not suitable for correlating two distinct variables.

C. Bar chart: A bar chart is designed to compare values across discrete categories, not to illustrate the relationship between two continuous variables like age and income.

D. Pie chart: A pie chart is used to display the proportional composition of a single categorical variable, representing parts of a whole, and cannot show a relationship between two variables.

1. Wilke

C. O. (2019). Fundamentals of Data Visualization. O'Reilly Media

Inc. In Chapter 12

"Visualizing associations among two or more quantitative variables

" the text states

"The standard visualization for an association between two quantitative variables is the scatterplot." The chapter proceeds to provide examples using this chart type.

2. University of Virginia Library

Research Data Services. (n.d.). Data Visualization: The Scatterplot. Retrieved from the University of Virginia Library website. The guide specifies

"A scatterplot is a type of data visualization that shows the relationship between two numerical variables... Scatterplots are used to observe and show relationships between two numeric variables."

3. Healy

K. (2018). Data Visualization: A Practical Introduction. Princeton University Press. In Chapter 3

"Make a Plot

" the section on scatterplots explains

"Scatterplots are the default choice for visualizing the relationship between two continuous variables" (p. 56).