Download Test #2 (Focusing on Chaps 3 and 4 but possibly ideas from Chaps

Test #2 (Focusing on Chapters 3 (3.1-3.6, 3.8) and 4 (4.1-4.4) but possibly ideas from Chaps 1 and 2)
Review Sheet MATH 2600
PART I <In class> Fill in the blank/Short answer
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Fill in the blank problems -- EXAMPLES
o A(n) ___________ is the collection of all possible outcomes from an experiment.
o A(n) ___________ is a number between ____ and ____ that measures the likelihood of an
outcome or event of an experiment.
State the requirements for a discrete probability distribution function
Interpret conditional probability (P(A|B)) and probabilities in general.
Other terms (not necessarily an exhaustive list ): independent events, simple event, compound event,
complement, probability , law of large numbers, probability of an outcome/event, mutually exclusive,
Bernoulli trial, binomial experiment, random variable (discrete and continuous), mean, variance,
standard deviation, expected value, tree diagrams, multiplication rule for counting, combinations,
permutations, partitions.
Be prepared to write out a definition and/or give an example and/or determine if a given example is
one of any of the following: Bernoulli trial, binomial experiment, independent events, conditional
probability, discrete random variable, discrete probability distribution function, discrete probability
distribution.
PART II (take home) You should be able to:
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Be familiar with probability ideas <Review Quiz #2>
o Determine the sample space of an experiment
o Find theoretical probabilities under "equally likely" assumption
o Determine the complement of an event
o Use the addition rule (all three forms)
o Use the multiplication rule (both forms)
o Determine if events are independent (three forms)
Be familiar with the following counting tools <permutations, combinations>
o tree diagrams
o addition and multiplication rules for counting
o combinations and permutations and partitions
Determine when a discrete probability distribution and/or discrete probability distribution function is
described (there are two requirements it must meet)
Find probabilities, mean µ(X), variance, standard deviation (X), and expected value E(X) = µ(X) of
a discrete random variable X given a corresponding discrete probability distribution p(x).
Bernoulli trials, binomial experiments/distribution
o Standard notation n, p, q, X, x, P(X=x)
Find probabilities, mean, standard deviation, expected value of binomial random variables
Determine if an event is "common" or "uncommon"
o use 2-standard deviations from mean as a measure