What type of variable is used to categorize time-series seasons?

The use of a categorical variable to classify time-series seasons is appropriate because seasons represent distinct and non-numeric categories. In a time-series analysis, seasons such as summer, winter, spring, and fall can be assigned labels or categories, which do not inherently possess a mathematical order. This allows researchers and analysts to group data based on these defined seasons, enabling better analysis of trends and patterns specific to each period.

Categorical variables are key in statistical analysis when dealing with qualitative data that can be divided into separate groups. Unlike continuous variables, which can take on an infinite number of values within a range, or dependent and independent variables that refer to specific measures in an experimental setting, categorical variables specifically serve to classify and organize data into identifiable categories.

In summary, categorizing time-series seasons with categorical variables enhances the ability to analyze data meaningfully, providing insights that would not be achievable with numerical representations.