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    Probability and Statistics
    MS-251
    Progress0 / 36 topics
    Topics
    1. Introduction: Statistics and Data Analysis2. Statistical Inference3. Samples, Populations, and the Role of Probability4. Sampling Procedures5. Discrete and Continuous Data6. Statistical Modeling7. Types of Statistical Studies8. Probability: Sample Space, Events, Counting Sample Points9. Probability of an Event10. Additive Rules11. Conditional Probability12. Independence and the Product Rule13. Bayes’ Rule14. Random Variables and Probability Distributions15. Mathematical Expectation: Mean of a Random Variable16. Variance and Covariance of Random Variables17. Means and Variances of Linear Combinations of Random Variables18. Chebyshev’s Theorem19. Discrete Probability Distributions20. Continuous Probability Distributions21. Fundamental Sampling Distributions22. Sampling Distributions and Data Descriptions23. Random Sampling24. Sampling Distributions25. Sampling Distribution of Means and the Central Limit Theorem26. Sampling Distribution of S227. t-Distribution28. F-Quantile and Probability Plots29. Single Sample & One- and Two-Sample Estimation Problems30. Single Sample & One- and Two-Sample Tests of Hypotheses31. The Use of P-Values for Decision Making in Testing Hypotheses32. Regression: Linear Regression and Correlation33. Least Squares and the Fitted Model34. Multiple Linear Regression and Certain Nonlinear Regression Models35. Linear Regression Model Using Matrices36. Properties of the Least Squares Estimators
    MS-251›Additive Rules
    Probability and StatisticsTopic 10 of 36

    Additive Rules

    9 minread
    1,614words
    Intermediatelevel

    Additive Rules in Probability

    The additive rules of probability describe how to calculate the probability of the union of two or more events. These rules help determine the probability that at least one of several events occurs. There are two key additive rules: the General Addition Rule and the Special Addition Rule. Let's explore both in detail.


    1. Special Addition Rule (for Mutually Exclusive Events)

    When two events are mutually exclusive (also known as disjoint), it means that they cannot both happen at the same time. For example, when rolling a die, the event of rolling a 2 and the event of rolling a 5 are mutually exclusive because you cannot roll both a 2 and a 5 in a single throw.

    Rule:

    If two events AAA and BBB are mutually exclusive, then the probability of either event occurring is the sum of their individual probabilities:

    P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B)P(A∪B)=P(A)+P(B)

    Where:

    • P(A∪B)P(A \cup B)P(A∪B) is the probability that event AAA or event BBB occurs.
    • P(A)P(A)P(A) is the probability of event AAA.
    • P(B)P(B)P(B) is the probability of event BBB.

    Example: Rolling a Die

    • Event AAA: Rolling a 2.
    • Event BBB: Rolling a 5.
    • These two events are mutually exclusive, because you cannot roll both a 2 and a 5 on the same die at the same time.
    P(A)=16,P(B)=16P(A) = \frac{1}{6}, \quad P(B) = \frac{1}{6}P(A)=61​,P(B)=61​

    So, using the Special Addition Rule:

    P(A∪B)=P(A)+P(B)=16+16=26=13P(A \cup B) = P(A) + P(B) = \frac{1}{6} + \frac{1}{6} = \frac{2}{6} = \frac{1}{3}P(A∪B)=P(A)+P(B)=61​+61​=62​=31​

    Thus, the probability of rolling either a 2 or a 5 is 13\frac{1}{3}31​.


    2. General Addition Rule (for Non-Mutually Exclusive Events)

    If the events are not mutually exclusive, meaning that both events can happen at the same time (they have an overlap), we need to adjust for the fact that we may have counted the overlapping outcomes twice.

    Rule:

    For two events AAA and BBB, the probability of either event occurring is:

    P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)P(A∪B)=P(A)+P(B)−P(A∩B)

    Where:

    • P(A∪B)P(A \cup B)P(A∪B) is the probability that either event AAA or event BBB occurs.
    • P(A)P(A)P(A) is the probability of event AAA.
    • P(B)P(B)P(B) is the probability of event BBB.
    • P(A∩B)P(A \cap B)P(A∩B) is the probability that both events AAA and BBB occur at the same time (i.e., the intersection of AAA and BBB).

    Why subtract P(A∩B)P(A \cap B)P(A∩B)?

    The reason we subtract P(A∩B)P(A \cap B)P(A∩B) is that it is included in both P(A)P(A)P(A) and P(B)P(B)P(B). Without subtracting it, we would be double-counting the probability of the overlap.

    Example: Drawing Cards from a Deck

    • Event AAA: Drawing a red card (26 red cards in a standard deck of 52 cards).
    • Event BBB: Drawing a face card (12 face cards in a standard deck of 52 cards).

    These two events are not mutually exclusive because there are red face cards (i.e., the 6 red face cards: Jack, Queen, and King of Hearts and Diamonds).

    P(A)=2652=12,P(B)=1252=313P(A) = \frac{26}{52} = \frac{1}{2}, \quad P(B) = \frac{12}{52} = \frac{3}{13}P(A)=5226​=21​,P(B)=5212​=133​

    Now, the probability of drawing a red face card (the intersection of AAA and BBB):

    P(A∩B)=652=326P(A \cap B) = \frac{6}{52} = \frac{3}{26}P(A∩B)=526​=263​

    Now, applying the General Addition Rule:

    P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)P(A∪B)=P(A)+P(B)−P(A∩B) P(A∪B)=12+313−326P(A \cup B) = \frac{1}{2} + \frac{3}{13} - \frac{3}{26}P(A∪B)=21​+133​−263​

    To simplify:

    P(A∪B)=1326+626−326=1626=813P(A \cup B) = \frac{13}{26} + \frac{6}{26} - \frac{3}{26} = \frac{16}{26} = \frac{8}{13}P(A∪B)=2613​+266​−263​=2616​=138​

    Thus, the probability of drawing either a red card or a face card is 813\frac{8}{13}138​.


    3. Additive Rule for Three or More Events

    The General Addition Rule can also be extended to more than two events. For three events AAA, BBB, and CCC, the rule is:

    P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)P(A \cup B \cup C) = P(A) + P(B) + P(C) - P(A \cap B) - P(A \cap C) - P(B \cap C) + P(A \cap B \cap C)P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)

    This rule can be extended for more events, but the key concept is the same: adding probabilities of individual events and subtracting the probabilities of their intersections to avoid double-counting.

    Example: Rolling Three Dice

    • Event AAA: Rolling a 1 on the first die.
    • Event BBB: Rolling a 1 on the second die.
    • Event CCC: Rolling a 1 on the third die.

    These events are not mutually exclusive, as multiple dice can show 1s simultaneously. To find the probability of rolling a 1 on at least one die, you would apply the General Addition Rule for three events.


    Summary of Additive Rules

    • Special Addition Rule (for Mutually Exclusive Events):

      P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B)P(A∪B)=P(A)+P(B)

      Use this when the events cannot happen at the same time (e.g., rolling a 2 or a 5 on a die).

    • General Addition Rule (for Non-Mutually Exclusive Events):

      P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)P(A∪B)=P(A)+P(B)−P(A∩B)

      Use this when the events can happen at the same time (e.g., drawing a red card or a face card from a deck).

    • Addition Rule for More Than Two Events: The general rule extends to more events and requires subtracting intersections and adding back the intersection of all events.

    The additive rules of probability are fundamental for calculating the likelihood of at least one of several events occurring, especially when events are not mutually exclusive.

    Previous topic 9
    Probability of an Event
    Next topic 11
    Conditional Probability

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