Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval.

A continuous system is one in which important activities of the system completes smoothly without any delay, i.e. no queue of events, no sorting of time simulation, etc. When a continuous system is modeled mathematically, its variables representing the attributes are controlled by continuous functions.

We could be infinitly accurate and use an infinite number of decimal places, therefore making age continuous. However, in everyday appliances, all values under 6 years and above 5 years are called 5 years old. So we use age usually as a discrete variable.

Variable Reference Table : Few Examples

Typology of Variables and Data

Variables such as heart rate, platelet count and respiration rate are in fact discrete yet are considered continuous because of large number of possible values. Only those variables which can take a small number of values, say, less than 10, are generally considered discrete.

A continuous distribution should have an infinite number of values between $0.00 and $0.01. Money does not have this property – there is always an indivisible unit of smallest currency. And as such, money is a discrete quantity.

For example, the concentration of glucose in the blood is a continuous quantity. Even if the instrument you are using rounds off values to whole numbers, these quantities are still continuous; ie, not countable.

Blood type is not a discrete random variable because it is categorical. Continuous random variables have numeric values that can be any number in an interval. For example, the (exact) weight of a person is a continuous random variable. Foot length is also a continuous random variable.

Amount of sleep is a variable. 3, 5, 9 hours of sleep are different values for that variable. Variables can be continuous or discrete. Question: Are these variables discrete or continuous?…Frequency distribution table:

when family size is measured as a continuous variable. Household size and number of computers are discrete variables. The range is an important descriptive statistic for a continuous variable, but it is based only on two values in the data set.

1. Shoe size is whole number (discrete), but the underlying measure is foot length which is measurement (continuous) data. Even half sizes are still not really measurement but “whole number”, because there is nothing between size 8 and 8 1/2.

The word discrete means countable. For example, the number of students in a class is countable, or discrete.

Yes, speed is a common continuous variable, and the value is chosen by a random process. We know it is continuous because there is always another possible value between any two speed values.

Discrete data is information that can only take certain values. Continuous data is data that can take any value. Height, weight, temperature and length are all examples of continuous data.

Key Difference Between Discrete And Continuous Variables The simplest similarity that a discrete variable shares with a continuous variable is that both are variables meaning they have a changing value. Besides that, they are also statistical terminologies used for comparative analysis.

Examples. The following are examples of discrete variables: Gender. Eye color (brown, blue, green, hazel)

It is also a discrete variable because one can simply count the number of phone calls made on a cell phone in any given day. The possible numbers are only integers such as 0, 1, 2, , 50, etc….Example 1.11 Variables.

Temperature is continuous variable as it does have fractional value too. For example: Today’s temperature is 30. 5 degree Celsius, here 30. 5 is not a discrete variable and hence is a continuous variable.

The simplest similarity that a discrete variable shares with a continuous variable is that both are variables meaning they have a changing value. Besides that, they are also statistical terminologies used for comparative analysis.

Other examples of continuous data are slopes, elevations, relative humidity, and atmospheric pressure.

Discrete quantitative variables can take on only a countable number of values, there are gaps between the possible choices. These are usually count variables of some kind. In our set above, blood pressure measurements and Body Mass Index (or BMI) would be clearly continuous.

Qualitative data contains categorical variables and quantitative data contains numerical variables. Categorical variables come in nominal or ordinal flavours, whereas numerical variables can be discrete or continuous.

Discrete data can be further sub-divided into three categories: binary, nominal and ordinal. Binary Data: A binary data only takes on two possible values. For example, lamp is on or lamp is off, answer is true or false, 0 or 1, yes or no etc.

A Discrete variable can take either a finite or a countable number of values. For example, the variable ” the number of children” is discrete and the variable ” GPA” is continuous. Since GPA can take an infinite number of possible values, for example interval 0.0 to 4.0.

The marital status of a person is a nominal qualitative variable to which a numerical value can not be assigned. A discrete variable is always numeric.

discrete (categorical) variables can be further classified into: ➢ nominal variable: no natural ordering between categories (e.g., religious affiliation, mode of transportation, favorite type of music, …) (e.g., size of automobile, social class, political philosophy, patient condition, …)

Ordinal (ordered) variables, e.g., grade levels, income levels, school grades. Discrete interval variables with only a few values, e.g., number of times married. Continuous variables grouped into small number of categories, e.g., income grouped into subsets, blood pressure levels (normal, high-normal etc)

So your college major is an categorical data.

In general, quantities such as pressure, height, mass, weight, density, volume, temperature, and distance are examples of continuous random variables.