Final answer:
Numerical and univariate fan data refers to a set of number-based observations on a single attribute - the number of fans. This contrasts with bivariate or multivariate data, which involve multiple characteristics. Examples can include the count of fans at events or the amount of sales in a month, and graphical representations help to easily understand large data sets.
Step-by-step explanation:
When we say that fan data are numerical and univariate, we mean that we have a data set that consists of numbers, which could be counts or measurements, representing one variable. Unlike bivariate or multivariate data, which involve two or more variables, univariate data includes observations on only a single characteristic or attribute. For instance, if we collect data on the number of fans present at various sports events, the data would be univariate because it is focused solely on one variable: the number of fans.
Quantitative discrete data are numerical values that result from counting and taking on certain specific values. Examples can include counts of daily phone calls, where possible values could be whole numbers like zero, one, two, and so on. Another example could be the monthly data points from a long-term data set; though there are many data points, they are univariate if they represent just one type of information, such as monthly sales amounts.
Data compression techniques, such as graphical representations, can provide an intuitive understanding of large sets of numerical, univariate data without overwhelming the observer - making it easier to comprehend trends and patterns over time from many data points, like several hundred monthly records over decades.