Final answer:
The question is about the levels of measurement for data, including Nominal, Ordinal, Interval, and Ratio scales, along with the distinction between qualitative (categorical) or quantitative (discrete or continuous) data.The correct option is a).
Step-by-step explanation:
The student is asking about the types of data used in statistical analysis. There are different levels of measurement for data, which include Nominal, Ordinal, Interval, and Ratio scales. Here are examples of each:
Nominal data is qualitative and includes unordered categories or labels such as the colors of crayons in a box.
Ordinal data is qualitative but can be ordered, such as the classification of athletic ability of soccer players with labels like superior, average, and above average.
Interval data has a definite ordering and measurable differences but no true zero point, similar to temperature readings like baking temperatures for dishes.
Ratio data is like interval data but has a meaningful zero point and ratio comparisons can be made, such as the number of times per week a facility is visited.
Additionally, data can be classified as either qualitative (categorical) or quantitative. Quantitative data is further divided into discrete or continuous data, depending on whether it can have an infinite number of values between any two values. An example of quantitative discrete data is the number of pairs of shoes you own, while an example of quantitative continuous data is the distance from your home to the nearest grocery store.