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.
Variables measured in monetary units
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.
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.
Your nationality, city that you live in, model of your car(s), colour of your eyes.
Nominal or category;Ordinal scale;Interval scale; andRatio scale.
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.
Nominal Variables
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.
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.
Age is acontinuousvariable because it can bemeasured with numbers. A categorical variable deals with nominal variables example male or female, political view, etc
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.
Your nationality, city that you live in, model of your car(s), colour of your eyes.
The answer depends on the variables. If the sizes were on a nominal scale - small, medium, large - for example, then a stacked bar with frequencies would probably be the best. Otherwise, frequency polygons or cumulative frequency charts will do.
Nominal values are the values that a component is specified to be. For example, the nominal value of a 10K resistor is 10K. Its actual value may vary, though, based on its tolerance.
Three, they are: Constant,dependant, & controlled. Alternatively, you can say there are 4: nominal, ordinal, interval, and ratio.