Your nationality, city that you live in, model of your car(s), colour of your eyes.
Variables measured in monetary units
Nominal variables are categories without a natural order or ranking. Examples include gender (male, female, non-binary), marital status (single, married, divorced), and types of cuisine (Italian, Chinese, Mexican). These variables are used to label or classify data and can be analyzed using frequency counts or mode. They do not possess numerical value or quantifiable differences.
Nominal variables are categorical variables that represent different categories without any inherent order, such as gender, race, or favorite color. In contrast, ordinal variables also represent categories but have a clear, meaningful order, such as rankings (e.g., satisfaction levels like "satisfied," "neutral," "dissatisfied"). While nominal variables categorize data, ordinal variables allow for comparison based on their rankings.
A real variable is a quantitative measure that can take on a wide range of numerical values, allowing for meaningful mathematical operations such as addition and subtraction; examples include height, weight, and temperature. In contrast, a nominal variable is a categorical measure that represents distinct categories without any inherent order or ranking; examples include gender, nationality, and colors. Essentially, real variables express quantities, while nominal variables classify data into groups.
The appropriate measure of dispersion for nominal variables is the mode, as it identifies the most frequently occurring category within the dataset. Since nominal variables represent distinct categories without a meaningful order, other measures of dispersion, such as range or standard deviation, are not applicable. In addition to the mode, frequency distribution can also provide insights into the distribution of nominal data.
Variables measured in monetary units
Nominal and ordinal variables are both qualitative or discrete variables. Nominal variables allow for only qualitative classification while an ordinal variable is a nominal variable, but its different states are ordered in a meaningful sequence.
Examples of nominal accounts are losses and expenses of gains or income.
Nominal Variables
Nominal variables are categories without a natural order or ranking. Examples include gender (male, female, non-binary), marital status (single, married, divorced), and types of cuisine (Italian, Chinese, Mexican). These variables are used to label or classify data and can be analyzed using frequency counts or mode. They do not possess numerical value or quantifiable differences.
Nominal variables are categorical variables that represent different categories without any inherent order, such as gender, race, or favorite color. In contrast, ordinal variables also represent categories but have a clear, meaningful order, such as rankings (e.g., satisfaction levels like "satisfied," "neutral," "dissatisfied"). While nominal variables categorize data, ordinal variables allow for comparison based on their rankings.
A real variable is a quantitative measure that can take on a wide range of numerical values, allowing for meaningful mathematical operations such as addition and subtraction; examples include height, weight, and temperature. In contrast, a nominal variable is a categorical measure that represents distinct categories without any inherent order or ranking; examples include gender, nationality, and colors. Essentially, real variables express quantities, while nominal variables classify data into groups.
The appropriate measure of dispersion for nominal variables is the mode, as it identifies the most frequently occurring category within the dataset. Since nominal variables represent distinct categories without a meaningful order, other measures of dispersion, such as range or standard deviation, are not applicable. In addition to the mode, frequency distribution can also provide insights into the distribution of nominal data.
There are many ways of categorising variables. One classification, used in statistics, is Nominal, Ordinal and Interval.
nominal and ordinal is wrong; those are the two types of qualitative variables. Ratio and interval are the two types of quantitative variables.
Categorical data refers to variables that can be divided into distinct groups or categories, while nominal data is a specific type of categorical data where the categories have no inherent order. Examples of categorical data include types of cuisine (Italian, Mexican, Chinese) and car brands (Ford, Toyota, Honda). An example of nominal data is gender (male, female, non-binary) or blood type (A, B, AB, O), where the categories do not have a ranked order.
A nominal variable is a type of categorical variable that represents distinct categories without any inherent order or ranking. Examples include gender, nationality, or favorite color, where the values serve to label different groups. Since nominal variables do not have a quantitative value, statistical analysis typically involves counting occurrences or determining proportions within each category.