Answer:
When analyzing a data set, it is important to identify the different types of variables present as well as the number of levels or categories within each variable. This helps reveal the underlying structure of the data.
Let's define some key terms:
Variables: Characteristics or measures that can take on different values for each observation in the data set.
Levels: The distinct categories or possible values that a variable can have.
Let's consider two examples:
A simple bar chart that shows the number of students in different faculties in a university:
In this case, the number of variables is two:
Faculty (Categorical Variable): This variable describes the faculty that each student belongs to. The levels of this variable are the different faculties in the university (e.g., "Arts", "Science", "Engineering", "Business").
Number of Students (Quantitative Variable): This variable measures the count of students in each faculty. It doesn't have levels because it's a numeric variable, not a categorical one.
A scatter plot that shows the relationship between age and income:
Here, there are also two variables:
Age (Quantitative Variable): This variable measures the age of the individuals in the data set. As a numeric variable, it doesn't have levels.
Income (Quantitative Variable): This variable measures the income of the individuals. Like age, it's a quantitative variable and doesn't have levels.
Hope this helps you! :)