Final answer:
The characteristics of data that change or vary over time are known as variables. Variables differ from facts, which are verifiable truths, and are crucial for observing changes in statistical analyses. Types of data, such as qualitative and quantitative, are essential concepts in understanding how data may vary.
Step-by-step explanation:
The characteristics of data that change or vary over time are known as variables. These are different from facts, which are truthful statements that can be proven or verified, and differ from supplies or vulnerabilities, which don't pertain to data's variation over time. Data characteristics that can vary are essential in statistical analysis, as they allow researchers and analysts to observe changes, trends, and patterns.
For example, in a dataset from a survey of high school seniors about their future plans, the response to whether they plan to attend a four-year college would be a variable. This is because the response can change from one student to another and could change over time if the survey were repeated yearly. The 50 percent of students who say they are attending a university would represent a statistic that summarizes this variable information from the sample.
Understanding the distinction between different types of data like qualitative, quantitative, discrete, and continuous is essential for data analysis. Qualitative data refers to categorized or labeled data, whereas quantitative data can be numeric, further divided into discrete (counted) and continuous (measured) subgroups. A variable is the aspect of data that can hold different values across different instances or time frames, thereby embodying variation within the dataset. B. Variables is the answer to this question.