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Chapter 3: Probability Topic

3.1 Terminology

  • Experiment: is a planned operation carried out under the controlled condition

  • Outcome: Result of an experiment

  • Sample space: a set of all possible outcomes

  • Event: any combination of outcomes that are usually represented by the letters A and B.

  • Equally Likely: Each outcome of an experiment has the same probability.

  • Probability: the chance of the outcome of an event

  • “OR” Event: an outcome is in the event A OR B if the outcome is in A or is in B or is in both A and B.

  • "AND" Event: an outcome is in the event A AND B if the outcome is in both A and B at the same time.

  • Conditional probability: A given B is written P(A|B). P(A|B) is the probability that event A will occur given that event B has already occurred

  • Complement: A given B is written P(A|B’)

Formula for P(A|B)

3.2 Independent and Mutually Exclusive Events

  • Independent: One event occurring does not affect the chance that the other event occurs.

  • Sampling with replacement: then events are considered to be independent, meaning the result of the first pick will not change the probabilities for the second pick.

  • Sampling without replacement: the probabilities for the second pick are affected by the result of the first pick. The events are considered to be dependent or not independent.

  • The events are independent if this is true:

    • P(A|B) = P(A)

    • P(B|A) = P(B)

    • P(A AND B) = P(A)P(B)

  • Mutually exclusive: If events occur at the same time

    • P(A and B) = 0

3.3 Two Basic Rules of Probability

  • Multiplication Rule: If A and B are two events defined on a sample space, then: P(A AND B) = P(B)P(A|B). A and B are independent, then P(A|B) = P(A). Then P(A AND B) = P(A|B)P(B) becomes P(A AND B) = P(A)P(B)

    • P(A|B) = 𝑃(𝐴 AND 𝐵) / 𝑃(𝐵)

  • Addition Rule: If A and B are defined on a sample space, then: P(A OR B) = P(A) + P(B) - P(A AND B). If A and B are mutually exclusive, then P(A AND B) = 0. Then P(A OR B) = P(A) + P(B) - P(A AND B) becomes P(A OR B) = P(A) + P(B)

3.4 Contingency Tables

  • Contingency table: provides a way of portraying data that can facilitate calculating probabilities.

3.5 Tree and Venn Diagrams

  • Tree diagrams: used to determine the outcomes of an experiment. It consists of "branches" that are labeled with either frequencies or probabilities.

  • Venn diagrams: a picture that represents the outcomes of an experiment. It generally consists of a box that represents the sample space S together with circles or ovals. The circles or ovals represent events.

Chapter 3: Probability Topic

3.1 Terminology

  • Experiment: is a planned operation carried out under the controlled condition

  • Outcome: Result of an experiment

  • Sample space: a set of all possible outcomes

  • Event: any combination of outcomes that are usually represented by the letters A and B.

  • Equally Likely: Each outcome of an experiment has the same probability.

  • Probability: the chance of the outcome of an event

  • “OR” Event: an outcome is in the event A OR B if the outcome is in A or is in B or is in both A and B.

  • "AND" Event: an outcome is in the event A AND B if the outcome is in both A and B at the same time.

  • Conditional probability: A given B is written P(A|B). P(A|B) is the probability that event A will occur given that event B has already occurred

  • Complement: A given B is written P(A|B’)

Formula for P(A|B)

3.2 Independent and Mutually Exclusive Events

  • Independent: One event occurring does not affect the chance that the other event occurs.

  • Sampling with replacement: then events are considered to be independent, meaning the result of the first pick will not change the probabilities for the second pick.

  • Sampling without replacement: the probabilities for the second pick are affected by the result of the first pick. The events are considered to be dependent or not independent.

  • The events are independent if this is true:

    • P(A|B) = P(A)

    • P(B|A) = P(B)

    • P(A AND B) = P(A)P(B)

  • Mutually exclusive: If events occur at the same time

    • P(A and B) = 0

3.3 Two Basic Rules of Probability

  • Multiplication Rule: If A and B are two events defined on a sample space, then: P(A AND B) = P(B)P(A|B). A and B are independent, then P(A|B) = P(A). Then P(A AND B) = P(A|B)P(B) becomes P(A AND B) = P(A)P(B)

    • P(A|B) = 𝑃(𝐴 AND 𝐵) / 𝑃(𝐵)

  • Addition Rule: If A and B are defined on a sample space, then: P(A OR B) = P(A) + P(B) - P(A AND B). If A and B are mutually exclusive, then P(A AND B) = 0. Then P(A OR B) = P(A) + P(B) - P(A AND B) becomes P(A OR B) = P(A) + P(B)

3.4 Contingency Tables

  • Contingency table: provides a way of portraying data that can facilitate calculating probabilities.

3.5 Tree and Venn Diagrams

  • Tree diagrams: used to determine the outcomes of an experiment. It consists of "branches" that are labeled with either frequencies or probabilities.

  • Venn diagrams: a picture that represents the outcomes of an experiment. It generally consists of a box that represents the sample space S together with circles or ovals. The circles or ovals represent events.

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