discrete

(adjective)

Separate; distinct; individual; non-continuous.

Related Terms

Examples of discrete in the following topics:

  • Probability Distributions for Discrete Random Variables

    • Probability distributions for discrete random variables can be displayed as a formula, in a table, or in a graph.
    • A discrete random variable $x$ has a countable number of possible values.
    • A discrete probability distribution can be described by a table, by a formula, or by a graph.
    • Sometimes, the discrete probability distribution is referred to as the probability mass function (pmf).
    • This histogram displays the probabilities of each of the three discrete random variables.

  • Common Discrete Probability Distribution Functions

    • Some of the more common discrete probability functions are binomial, geometric, hypergeometric, and Poisson.

  • Student Learning Outcomes

  • Two Types of Random Variables

    • A random variable $x$, and its distribution, can be discrete or continuous.
    • Random variables can be classified as either discrete (that is, taking any of a specified list of exact values) or as continuous (taking any numerical value in an interval or collection of intervals).
    • Discrete random variables can take on either a finite or at most a countably infinite set of discrete values (for example, the integers).
    • Examples of discrete random variables include the values obtained from rolling a die and the grades received on a test out of 100.
    • This shows the probability mass function of a discrete probability distribution.

  • Probability Distribution Function (PDF) for a Discrete Random Variable

    • This is a discrete PDF because

  • Introduction

    • These two examples illustrate two different types of probability problems involving discrete random variables.
    • Recall that discrete data are data that you can count.
    • In this chapter, you will study probability problems involving discrete random distributions.

  • Practice 1: Discrete Distribution

  • The Discrete Fourier Transform

    • Suppose we have discrete data, not a continuous function.
    • This is the discrete version of the Fourier transform (DFT).
    • In the handout you will see some Mathematica code for computing and displaying discrete Fourier transforms.

  • Expected Values of Discrete Random Variables

    • A discrete random variable $X$ has a countable number of possible values.
    • The probability distribution of a discrete random variable $X$ lists the values and their probabilities, such that $x_i$ has a probability of $p_i$.
    • The weights used in computing this average are probabilities in the case of a discrete random variable.

  • Types of Variables

    • Numeric variables may be further described as either continuous or discrete.
    • A discrete variable is a numeric variable.
    • A discrete variable cannot take the value of a fraction between one value and the next closest value.
    • Variables can be numeric or categorial, being further broken down in continuous and discrete, and nominal and ordinal variables.
    • Distinguish between quantitative and categorical, continuous and discrete, and ordinal and nominal variables.