chapter 6 OP STAT TERMS
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| probability | show 🗑
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| random process | show 🗑
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| show | generates outcomes that are determined purely by chance.
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| law of large numbers | show 🗑
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| show | imitates a random process in such a way that simulated outcomes are consistent with real-world outcomes.
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| event | show 🗑
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| show | says that
𝑃(A𝐶)=1−𝑃(A, where A𝐶 is the complement of event A; that is, the event that A does not occur.
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| Venn diagram | show 🗑
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| Conditional probability | show 🗑
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| independent events | show 🗑
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| show | For any random process, the probability that events A and B both occur can be found using the general multiplication rule: P ( A and B) = P ( A n B) = P(a)x p(b/a)
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| tree diagram | show 🗑
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| show | takes numerical values that describe the outcomes of a random process.
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| show | of a random variable gives its possible values and their probabilities.
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| show | X takes a fixed set of possible values with gaps between them.
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| show | is its average value over many, many trials of the same random process.
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| standard deviation of a discrete random variable | show 🗑
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| continuous random variable | show 🗑
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| Independent random variables | show 🗑
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| show | The count of successes X in a binomial setting is a binomial random variable. The possible values of X are 0, 1, 2, …, n.
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| show | The probability distribution of X is a binomial distribution. Any binomial distribution is completely specified by two numbers: the number of trials n of the random process and the probability p of success on each trial.
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| Binomial coefficient | show 🗑
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| show | When taking a random sample of size n from a population of size N, we can treat individual observations as independent when performing calculations as long as n<.10 N
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| Large Counts condition | show 🗑
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