Statistics Ch 9

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  1. random
    if individual outcomes are uncertain, but there is nonetheless a regular distribution of outcomes in a large number of repetitions.
  2. probability
    of any outcome of a random phenomenon can be defined as the proportion of times the outcome would occur in a very long series of repetitions.
  3. Probability models
    Mathematically describe the outcome of random processes. They consist of two parts:

    • 1) S = Sample Space: a set, or list, of all possible outcomes of a random process.
    • An event is a subset of the sample space.

    2) A probability for each possible event in the sample space S.
  4. Probability rules
    1) Probabilities range from 0 (no chance of the event) to 1 (the event has to happen).

    For any event A, 0 ≤ P(A) ≤ 1

    2) The probability of the complete sample space must equal 1.

    P (sample space) = 1

    3) The probability of an event not occurring is 1 minus the probability that does occur.

    P(A) = 1 – P(not A)

    • 4) Two events A and B are disjoint if they have no outcomes in common and can never happen together. The probability that A or B occurs is the sum of their individual
    • probabilities.

    P(A or B) = “P(A U B)” = P(A) + P(B)

    This is the addition rule for disjoint events.
  5. addition rule for disjoint events
    P(A or B) = “P(A U B)” = P(A) + P(B)
  6. Discrete sample space
    deals with data that can take on only certain values. These values are often integers or whole numbers
  7. Continuous sample space
    • contains an infinite number of events. They typically are
    • intervals of possible, continuously-distributed outcomes.

Card Set Information

Statistics Ch 9
2012-02-25 17:42:05

Introducing probability
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