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  • Perplex
    IB Math AASL
    /
    Distributions & Random Variables
    /

    Discrete random variables

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    Exercises

    Key Skills

    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.

    Practice exam-style discrete random variables problems

    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.