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Random Variable
Value that represents the outcome of a trial/ experiment/ event
functions or rules that assign numerical values to each outcome in the experiment’s sample space
X = random variable in general
Xi = specific value of X
Probability distribution function
Shows probabilities of different possible value of a variable
For discrete, it shows the probability of each possible value of the variable
Describes likelihood of obtaining the possible values
Law of probability
Probability that I choose each individual item (or probability of each outcome) must equal 1
Cumulative distribution function (CDF)
Shows cumulative sum of probabilities from smallest to largest possible values of X
Can’t go over 1, constantly increasing until 1 and tapers off after
Expected value of random variable X =
Weighted average
E(X) is sum of all possible values of X weighted by respective probabilities
mean of probability distribution
Binomial distribution
Used with exactly 2 mutually exclusive outcomes
Dichotomous → true/ false, yes/ no, etc.
Bimodal characteristics
fixed number of trials n
only two possible outcomes
probability of success for each trial remains constant
trials are independent from each other
random variable X represents number of successes out of n trials
How to calculate binomial PDF in Excel
=BINOM.DIST ( )
cumulative probability = ‘TRUE’
Poisson Distribution
Shows how many times an event is likely to occur/ arrive over ap eriod of time
random variable X is the number of events/ arrivals per unit of time
parameter is (wishbone symbol), is mean number of events per unit of time in space
Poisson characteristics
event occurs randomly over time or space
Average arrival rate (wishbone figure) remains constant
arrivals independent of each other
random variable X represents the number of arrivals within observed time interval
always right-skewed → discrete but still infinite, would go on forever but never touch 0
Poisson distribution in Excel
=POISSON.DIST ( )
TRUE for cumulative function