Unit 6: Anticipating Patterns

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46 Terms

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Disjoint

or mutually exclusive events: events that have no outcome in common.

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Complement

: the set of all possible outcomes in a sample space that do not lead to the event.

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Intersection

: events A and B is the set of all possible outcomes that lead to both events A and B.

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Parameter

: a numerical measurement describing some characteristic of a population.

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Central limit theorem

: If the sample size is large enough then we can assume it has an approximately normal distribution.

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n trials

The are independent and are repeated using identical conditions.

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sample size

The has to be greater than 30 to assume an approximately normal distribution.

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Tree diagram

: representation is useful in determining the sample space for an experiment, especially if there are relatively few possible outcomes.

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Standard deviation

is the of the original distribution.

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Standard deviation

is the of the original distribution.

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B

Union: events A and is the set of all possible outcomes that lead to at least one of the two events A and .

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original distribution

Mean is the mean of the .

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original distribution

Mean is the mean of the .

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Sample space

: a set of all possible outcomes.

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Standard error

: standard deviation of the distribution of the statistics.

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original distribution

Mean is the mean of the .

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Statistic

: a numerical measurement describing some characteristic of a sample.

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Probability

the chance of the outcome of an event

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Sample space

a set of all possible outcomes

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Tree diagram

representation is useful in determining the sample space for an experiment, especially if there are relatively few possible outcomes

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Rule 1

For any event A, the probability of A is always greater than or equal to 0 and less than or equal to 1

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Rule 2

The sum of the probabilities for all possible outcomes in a sample space is always 1

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Impossible event

If an event can never occur, its probability is 0

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Sure event

Of an event must occur every time, its probability is 1

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"Odds in favor of an event"

ratio of the probability of the occurrence of an event to the probability of the nonoccurrence of that event

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Complement

the set of all possible outcomes in a sample space that do not lead to the event

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Disjoint or mutually exclusive events

events that have no outcome in common

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Union

events A and B is the set of all possible outcomes that lead to at least one of the two events A and B

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Intersection

events A and B is the set of all possible outcomes that lead to both events A and B

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Conditional Events

A given B is a set of outcomes for event A that occurs if B has occurred

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Variable

quantity whose value varies from subject to subject

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Probability experiment

an experiment whose possible outcomes may be known but whose exact outcome is a random event and cannot be predicted with certainty in advance

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Random variables

The outcome of a probability experiment takes a numerical value

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Discrete random variable

quantitative variable that takes a countable number of values

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Continuous random variable

a quantitative variable that can take all the possible values in a given range

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Expected value

Computed by multiplying each value of the random variable by its probability and then adding over the sample space

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Variance

sum of the product of squared deviation of the values of the variable from the mean and the corresponding probabilities

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Combination

the number of ways r items can be selected out of n items if the order of selection is not important

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The continuous probability distribution (cdf)

graph or a formula giving all possible values taken by a random variable and the corresponding probabilities

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Parameter

a numerical measurement describing some characteristic of a population

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Statistic

a numerical measurement describing some characteristic of a sample

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Sampling distribution

the probability distribution of all possible values of a statistic, different samples of the same size from the same population will result in different statistical values

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Standard error

standard deviation of the distribution of the statistics

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Central limit theorem

If the sample size is large enough then we can assume it has an approximately normal distribution

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Mean

μ = 1/p

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Standard Deviation

σ = √1/𝑝(1/𝑝−1)