Final answer:
Quantitative discrete data refers to countable numerical values, such as the number of shoes owned, classes taken, or tickets sold, whereas qualitative data deals with non-numeric characteristics. Quantitative continuous data involves measurable ranges. Properly categorizing data allows comparisons with theoretical distributions, important in statistical analysis.
Step-by-step explanation:
To address the question about what type of record you need to build so common discrete results can be applied to a test, let's consider the various forms of data. Data that result from counting, such as the number of correct answers on a quiz, are called quantitative discrete data. This type of data takes on specific numerical values that are countable and finite in nature.
For example, quantitative discrete data could be:
- The number of pairs of shoes you own.
- The number of classes you take per school year.
- The number of tickets sold to a concert.
Alternatively, when you measure something and can obtain a range of values, it is referred to as quantitative continuous data, such as the distance from your home to the nearest grocery store. On the other hand, qualitative data involves non-numeric characteristics or qualities, like the type of car you drive or the type of calculator you use.
Understanding these distinctions is crucial in fields like statistics, and is especially applicable in statistics courses such as Stats AB. This knowledge helps us compare empirical data with a theoretical distribution to see if it fits a discrete or continuous model.