Business Statistics Chapter 4 – Flashcards

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Probability
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Numerical measure of the likelihood that an event will occur, assigned 0 to 1.
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What are ALWAYS the Probability values assigned on a scale?
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Zero to One.
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The Probability assigned to each experimental outcome must be between:
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Zero to One inclusively.
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Zero < P(E i) <1 for all i
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E i is the i th experimental outcome P(E i) is its probability
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The same of the probabilities for all experimental outcomes must equal:
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One
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P(E1)+P(E2)+....+(P(En)=__
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1
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A Probability near ____ indicates an event/outcome is almost certain to occur.
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Near One.
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A Probability near ___ indicates an event/outcome is almost unlikely to occur.
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Near Zero.
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Statistical experiments are also referred to as ___.
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Random Experiments.
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Experiment
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Process that generates well-defined outcomes.
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Sample Space
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Set of all experimental outcomes.
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Sample Point
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Another name for an experimental outcome.
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Experiment: Toss a coin Inspection a part Conduct a sales call Roll a die Play a football game
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Experiment Outcomes Head, tail Defective, non-defective Purchase, no purchase 1, 2, 3, 4, 5, 6 Win, lose, tie
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Tree diagram
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Graphical representation that helps in visualizing a multiple-step experiment.
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Basic requirements for assigning probabilities
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probability assigned to each experimental outcome must be between 0 and 1. the sum of the probabilities for all the experimental outcomes must equal 1
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Sources of Probability Methods: (list 3)
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1. Classical 2. Relative Frequency 3. Subjective
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Classical Method
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**equally likely outcomes** all the experimental outcomes are equally likely. Just putting
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Relative Frequency Method
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**Experimentation/Historical Data** Data are available to estimate the proportion of the time the experimental outcome will occur if the experiment is repeated a large number of times.
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Subjective Method
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**Judgment** When one cannot realistically assume that the experimental outcomes are equally likely
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Event
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collection of sample points
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What is equal to the sum of the probabilities of the sample points in the event?
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Probability of any event.
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Bayes' theorem
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provides a means for combining subjectively determined prior probabilities with probabilities obtained by other means to obtain revised probabilties
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Complement of A
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defined to be the event consisting of all sample points that are not in A Looks like: A^c
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Union of A and B
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the event containing all sample points belonging to A or B or BOTH Looks like: A U B
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Intersection of A and B
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the event containing the sample points belonging to both A and B Looks like: A ∩ B
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Addition Law
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a way to compute the probability that event A or event B or BOTH A and B occur.
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Addition Law Equation
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P (A U B)=P(A)+P(B)-P(A∩B)
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Mutually exclusive events
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two events are said to be mutually exclusive if the events have no sample points in common
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Conditional probability
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considering the probability of an event A given the condition that event B has occurred Looks like: P(A|B)
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Joint probabilities
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each of the values gives the probability of the intersection of two events
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Marginal probabilities
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summing the joint probabilities in the corresponding row or column of the joint probability table
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Independent events
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if A given B equals A, and B given A equals B (ignore the middle letter)
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Multiplication Law
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used to compute the probability of the intersection of two events Looks like: P(A ∩ B) = P(B) P(A|B)
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