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random process
generates outcomes that are determined by chance
unpredictable in the short run
eventually has a regular and predictable pattern in the long run
probability
a number between 0 and 1 that describes the proportion of times the outcome would occur in a very long series of repetitons
express as decimal, fraction, or percentage.
empirical probability
likelihood of an event occurring based on actual experiments or observations
use approximates for real trial
theoretial probability
calculated likelihood of an event occurring, based on the mathematical model rather than experimental data
P(E) = (# of ways an event can occur)/(total possible outcome)
law of large numbers
as the number of trials increases, the relative frequency approaches theoretical probability
basic rules of probability
if all outcomes in sample space are equally likely, the probability that event A occurs is P(A) = (# of outcomes in event A)/(total # outcomes in sample space
probability of any even is a number between 0 and 1
all possible outcomes together must have probabilities that add up to 1
the probability that an event does not occur is 1 minus probabillity of the event occuring
complement rule
probability that an event will NOT occur
1 minus the probability of the event is the complement
P(Ac) = 1 - P(A)
law of averages
a common misconception of past outcomes of a random event being able to influence future results
“balancing out”
misguided belief
sample space
list of all possible outcomes
simulation
imitation of chance behavior, based on a model that accurately reflects the situation
assign numbers
determine what you are counting and record
probability model
description of some chance process that MUST consist of two parts
sample space
probability for each outcome
event
any collection of outcomes from some chance process
mutually exclusive (disjoint)
two events that have no common outcomes, can never occur together
P(A and B) = 0
NEVER independent
visual images for summarizing sample space
can make either two-way table or venn diagram
make sure to properly isolate each response in the proper section
P(A U B)
union; probability that either event A, event B, or both events occur
P(A) + P(B) - P(A ∩ B)
conditional probability
probability that one event occurs given that another event is known to happen
P(A|B)
calculating conditional probability
P(A|B) = P(A ∩ B) ÷ P(B)
independent events
if knowing whether or not one event has occurred does not impact the probability that the other event will happen
yes: P(A) = P(A|B)
no: P(A) ≠ P(A|B)
must be exact same value
multiplication rule
P(A ∩ B) = P(A) × P(B)
specifically only for independent events
tree diagram
shows the sample space of a chance process involving multiple stages
Every probability after the first stage is conditional
general formula for showing independence
P(A) ≠ P(A|B)
Therefore, the events “A” and “B” are not independent. Knowing that “B” happened increases/decreases the probability of “A.”
increases —> value is greater than 50%
decreases —> value is less than 50%
general formula for discussing simulation results
On average, according to my simulation, ____.
must include units if available
include the real mean, even if in parentheses