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

·  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:

·  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 s(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