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the chance that an event will occur |
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A Priori/ Emperical Eperiamental Baysian |
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- based on prior knowledge of the process involved
- based on formulas that you can use before an event occurs
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based upon prior information (hunches) |
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Simple Events (Marginal Events) |
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events than can be described by one characteristic (mutually exclusive) |
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Collection of all possible events (collectively exhaustive) |
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a process that results in the selection of only one of many possible outcomes with uncertaintly as to which outcome will occur |
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First Axion of Probability |
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Second Axion of Probability |
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P(Ei or Ei) = P(Ei) + P(Ei) |
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Third Axion of Probability |
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an event that requires two or more characterisitcs to describe |
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the intersection of two events (A & B) is where both event A & event B occur |
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the union of tow events A & B is where either A occurs, B occurs, or both occur |
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If two events are on the same axis they are |
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If two events are not on the same axis, they are |
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If two events are statistically independent, having knowledge about "B".... |
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doesn't give you information about "A" |
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If two events are statistically dependent, having knowledge about "B" |
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does provide information about "A" |
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Two events are statistically independent if and only if: (Equation) |
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Statistically independent (equation) |
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P (A ∩ B) = P (A and B) = P(A)P(B) |
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Statistically Dependent (Equation) |
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P(A ∩ B) = P(A and B) = P(A¦B)P(B) |
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How to Prove Statistical Independence |
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1. Find P(A¦B) 2. Find P(A) 3. if 1=2 A&B are statistically independent or if 1≠2 A&B are statistically dependent |
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If you sample with replacement, all events are statistically.... |
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Definition
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The urn never changes when sampling.... |
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If you sample without replacement, all events are statistically |
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Contingency Tables (marginal and intersection) |
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- marginal on the outside -intersection on the inside |
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Different ways to express joint events |
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A intersect B A and B A ∩ B |
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P(A U B) = P(A or B)= P(A) + P(B) - P(A ∩ B) |
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P(A U B) =P(A or B) = P(A) + P(B) |
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the second way to present a sample space. * Represents the various events as "unions" and "intersections" of circles |
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a way to present a sample space * uses a table of cross-classifications to present a sample space |
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* the outcomes are based on observed data *NOT ON PRIOR KNOWLEDGE of a process |
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*differs from person to person |
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P(A and B) P(A) P(A¦B)P(B) P(A) |
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if both events cannot occur simultaneously (the result of a coin toss cannot simultaneously be a head and a tail) |
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Cellectively Exhaustive Events |
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one of the events must occur ( if heads doesn't occu, tails must occur) |
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the probability of event A, given information about the occurence of another event B P(A¦B)= P(A and B) P(B) |
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Three Axioms of Probability states: |
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Given N simple events that are collectively exhaustive (mutually exclusive) |
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