categorical
In categorical data, the concept of a midpoint is not applicable as it is in numerical data. Categorical data consists of distinct categories or groups without a meaningful order or numerical value, making it impossible to calculate a midpoint. Instead, you can analyze categorical data using measures such as mode, frequency distribution, or proportions to understand the distribution of categories.
Salary is typically considered numerical data because it represents a measurable quantity and can be expressed in numbers. Specifically, it is continuous numerical data, as salaries can take on a wide range of values. However, in some contexts, salaries may be categorized (e.g., salary ranges or levels) for analysis, making them categorical data. Overall, the classification depends on how the data is being used or represented.
A girl's size 6 shoe is the same in a women's size. After a size 13 in a toddler, child sized shoe, the sizes then are adult sizes.
A woman's size 5 shoe in the US is usually about 8.5 inches, while in the UK a size five shoe is 9.25 inches. Keep in mind however, that shoe sizing varies from brand to brand.
im pretty sure its a 4, because my girl's size is a 2 and I'm a women's 5
No
Categorical
yes because if you have categorical data you need the range for the value of the numbers so it would be the same for numerical data
Categorical
Can the median and mode be used to describe both categorical data and numerical data
Not sure about steam-and-leaf but a stem-and-leaf plot is used for numerical data.
A first name is considered categorical data, as it falls into distinct categories and does not have a numerical value.
In categorical data, the concept of a midpoint is not applicable as it is in numerical data. Categorical data consists of distinct categories or groups without a meaningful order or numerical value, making it impossible to calculate a midpoint. Instead, you can analyze categorical data using measures such as mode, frequency distribution, or proportions to understand the distribution of categories.
Mode.
mean
mode
false