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Probability Distributions
The process in which the trials are statistically independent and each trial of event has only two outcomes is classified as
Bernoulli process
Bayes process
functional process
independent limited process
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Probability Distributions
If the random samples are drawn without replacement and the population from which samples are drawn is infinite then the method which is not applicable is
weighted error probability distribution
hyper geometric probability distribution
Bernoulli probability distribution
asymmetrical random distribution
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Probability Distributions
The selling price of product is subtracted from purchasing price of product to calculate
profit of product
loss of profit
cumulative average
weighted average
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Probability Distributions
The probability of success increases in the binomial probability distribution if the value of
p increases
p decrease
q increases
q decreases
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Probability Distributions
The tail or head, the one or zero and the girl and boy are examples of
non-functional events
complementary events
non complementary events
functional events
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Probability Distributions
The probability of failure in binomial distribution is denoted by
p = q +1
p = q – 1
q = 1 + p
q = 1 – p
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Probability Distributions
The formula of calculating the variance for negative binomial distribution is
rq ⁄ p²
pq ⁄ r²
rp ⁄ q²
rq ⁄ p
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rikazzz
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Probability Distributions
In binomial distribution, the formula of calculating mean is
μ = p + q
μ = np
μ = pq
μ = qn
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rikazzz
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Probability Distributions
In a negative binomial distribution of probability, the random variable is also classified as
discrete random variable
continuous waiting time random variable
discrete waiting time random variable
discrete negative binomial variable
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rikazzz
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Probability Distributions
If the value of m in beta distribution is 35 and the value of n in beta distribution is 50 then the expected value of random variable x in beta distribution is
0.411
0.311
0.511
0.211
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