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How does a discrete probability distribution differ from a continuous probability distribution?
A discrete probability distribution is defined over a set value (such as a value of 1 or 2 or 3, etc). A continuous probability distribution is defined over an infinite number of points (such as all values between 1 and 3, inclusive).
The probability density function for a uniform distribution ranging between 2 and 6 is?
4
What are the requirements for probability distribution?
(1) That the probabilities lie between 0 and 1. (2) The sum of all probabilities of the distribution sum up to 1.
What is the probability that exactly 2 have some kind of defect?
The probability is determined by the binomial distribution. We consider p = probability of defect, q = probability of not defect, n = sample size, and x= number of defects in sample, in this case x=2. We calculate the probability as P(X = x) = n!/[(n-x)! x!] pxqn-x If sample size = 10 and p = 0.1 then: P(x= 2) = 10!/(8!x2!)(0.1)2(0.9)8 = 0.1937 You can find more about the binomial distribution under Wikipedia. It is important also to note the assumptions when using this distribution. It must be a random sample and the probability of defects is known.
What is the probability of a class running between 51.25 and 51.5 minutes if the uniform distribution is between 50 and 52?
The probability is (51.5-51.25)/(52-50) = 0.25/2 = 0.125
