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  • Perplex
    IB Math AASL
    /
    Distributions & Random Variables
    /

    Discrete random variables

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    Discrete random variables

    Discrete random variables

    Introduction and properties of random variables, probability distributions, expected value and variance, linear transformations of r.v.'s

    Want a deeper conceptual understanding? Try our interactive lesson!

    Exercises

    No exercises available for this concept.

    Key Skills

    Concept of a random variable
    SL 4.7

    A random variable is a variable that can take different values, each associated with some probability, arising from a random process or phenomenon. We usually denote random variables with a capital letter, such as X.


    It can be discrete (taken from a finite set of values) or continuous (taking any value within an interval).


    The probability distribution of a random variable tells us how likely each outcome is.

    Concept of a discrete random variable
    SL 4.7

    A discrete random variable takes from a finite set of values:

    X∈{x1​,x2​…xn​}

    where each possible value has an associated probability.

    Discrete probabilities sum to 1
    SL 4.7

    The sum of the probabilities for all possible values {x1​,x2​,…xn​} of a discrete random variable X equals 1. In symbols,

    P(U)  ​=P(X=x1​)+P(X=x2​)+...+P(X=xn​)=x∑ ​P(X=x)=1​

    where U denotes the sample space.

    Discrete probability distributions in a table
    SL 4.7

    Probability distributions of discrete random variables can be given in a table or as an expression. As a table, distributions have the form

    x

    x1​

    x2​

    ...

    xn​

    P(X=x)

    P(X=x1​)

    P(X=x2​)


    P(X=xn​)

    where the values in the row P(X=x) sum to 1.

    Discrete probability distributions as an expression
    SL 4.7

    Probability distributions can be given in a table or as an expression. As an expression, distributions have the form

    P(X=x)=(expression in x),x∈{set of possible x}

    for any discrete random variable X.

    Expected Value
    SL 4.7

    The expected value of a discrete random variable X is the average value you would get if you carried out infinitely many repetitions. It is a weighted sum of all the possible values:

    E(X)=∑x⋅P(X=x)📖

    The expected value is often denoted μ.

    Fair Games
    SL 4.7

    In probability, a game is a scenario where a player has a chance to win rewards based on the outcome of a probabilistic event. The rewards that a player can earn follow a probability distribution, for example X, that governs the likelihood of winning each reward. The expected return is the reward that a player can expect to earn, on average. It is given by E(X), where X is the probability distribution of the rewards.


    Games can also have a cost, which is the price a player must pay each time before playing the game. If the cost is equal to the expected return, the game is said to be fair.