Poisson Distribution:
The Poisson distribution is used to calculate the number of occurrences of an event over a specified interval of time or
Sampling distribution of :
When using simple random sampling, the expected value of is the population mean .
The standard deviation of (often called the standard error of the mean) is given by the formula,
for finite populations.
Sampling distribution of p:
The expected value of p is , the population proportion.
The standard deviation of p is, for finite populations.
86. The exponential distribution is related to the Poisson distribution: if the distribution of the time between events is
exponential, then the number of events occurring during an interval of time is Poisson.
For example, if the average time between the arrivals of customers in a department store is exponential with a mean of 2
minutes, then the average number of arrivals per minute is Poisson with a mean of 1/2 arrivals/minute.
87. The following rules apply to calculating probabilities of events:
Rule 1: The probability of any event is the sum of the probabilities of the outcomes that compose that event.
Rule 2: The probability of the complement of any event A is P(Ac) = 1 – P(A).
Rule 3: If events A and B are mutually exclusive, then P(A or B) = P(A) + P(B).
Rule 4: If two events A and B are not mutually exclusive, then P(A or B) = P(A) + P(B) – P(A and B). Here, (A and B)
represents the intersection of events A and B; that is, all outcomes belonging to both A and B.
88. Using Excel functions,
Mean = AVERAGE(45, 42, 56, …, 48) = 46.47
Median = MEDIAN(45, 42, 56, …, 48) = 45
Mode = MODE.SNGL(45, 42, 56, …, 48) = 45
Range = MAX(45, 42, 56, ..., 48) – MIN(45, 42, 56, …, 48) = 19
Sample variance = VAR.S(45, 42, 56, …, 48) = 33.84
Sample standard deviation = STDEV.S(45, 42, 56, …, 48) = 5.82
89. Binomial Distribution:
The binomial distribution describes the probability of obtaining exactly x “successes” in a sequence of n identical
experiments, called trials.