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Probability and Independence

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    64202
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    Probability

    For an experiment we define an event to be any collection of possible outcomes.     

    A simple event is an event that consists of exactly one outcome.  

        or:  means the union i.e.  either can occur

        and:  means intersection i.e. both must occur

     

    Two events are mutually exclusive if they cannot occur simultaneously.  

    For a Venn diagram, we can tell that two events are mutually exclusive if their regions do not intersect

    We define Probability  of an event E to be to be 

    \[ P(E) = \frac{ \text{number of simple events within E}}{\text{total number of possible outcomes}}\]

    We have the following:

    1. P(E) is always between 0 and 1.
       
    2. The sum of the probabilities of all simple events must be 1.
       
    3. P(E) + P(not E) = 1
       
    4. If E and F are mutually exclusive then 

             
      P(E or F) = P(E) + P(F)

     


    The Difference Between And and Or

    If E and F are events then we use the terminology

            E and F 

    to mean all outcomes that belong to both E and F

     

    We use the terminology

            E Or F 

    to mean all outcomes that belong to either E or F.

     

    Example

    Below is an example of two sets, A and B, graphed in a Venn diagram.  

    Venn diagram with sets A and B intersecting

    The green area represents A and B while all areas with color represent A or B

     


    Example

        Our Women's Volleyball team is recruiting for new members.  Suppose that a person inquires about the team.

            Let E be the event that the person is female

            Let F be the event that the person is a student

    then E And F represents the qualifications for being a member of the team.  Note that E Or F is not enough.

     

        We define

     

     

              Definition of Conditional Probability

    \[P(E|F) = \frac{P(E and F)}{P(F)}\]

        

    We read the left hand side as 

            "The probability of event E given event F"

    We call two events  independent if

     

     

    For Independent Events

    P(E|F)  =  P(E)

        

    Equivalently, we can say that E and F are independent if

     

     

    For Independent Events

    P(E and F)  =  P(E)P(F)

        

    Example

        Consider rolling two dice.  Let 

                E be the event that the first die is a 3.

                F be the event that the sum of the dice is an 8.

        Then E and F means that we rolled a three and then we rolled a 5

        This probability is 1/36 since there are 36 possible pairs and only one of them is (3,5)

        We have 

                P(E)  =  1/6  

        And note that (2,6),(3,5),(4,4),(5,3), and (6,2) give F

        Hence 

                P(F)  =  5/36

        We have 

                P(E) P(F) = (1/6) (5/36) 

        which is not 1/36.  

        We can conclude that E and F are not independent.

     


    Exercise

        Test the following two events for independence:

        E the event that the first die is a 1.

        F the event that the sum is a 7.

     


    A Counting Rule

    For two events, E and F, we always have

            P(E or F)  =  P(E) + P(F) - P(E and F)

     

    Example

    Find the probability of selecting either a heart or a face card from a 52 card deck.

     

    Solution

    We let

            E  =  the event that a heart is selected 

            F  =  the event that a face card is selected

    then 

            P(E)  =  1/4        and         P(F)  =  3/13   (Jack, Queen, or King out of 13 choices)

            P(E and F)  =  3/52

    The formula gives

            P(E or F)  =  1/4 + 3/13 - 3/52  =  22/52 =  42%

     


    Back to the Probability Home Page

     

     

    Probability and Independence is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

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