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13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive d) Equally likely

13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive d) Equally likely

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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12. The events having no experimental outcomes in common is called: a) Equally likely events e b) Exhaustive events c) Mutually exclusive events e d) Independent events 13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive e d) Equally likelye 14. The probability density function of a Markov process is a) p(x1,x2,x3..xn) = p(x1)p(x2/x1)p(x3/x2)...p(xn/xn-1)e b) p(x1,x2,x3.xn) = p(x1)p(x1/x2)p(x2/x3)..p(xn-1/xn)- c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)e d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)...p(xn*xn-1)e ..xn) = ..... 15. The discrete probability distribution in which the outcome is very small with a very small period of time is classified as « a) Posterior distribution b) Cumulative distributione c) Normal distributione d) Poisson distributione 16. In a Poisson Distribution, the mean and variance are equal. a) True b) False
Transcribed Image Text:12. The events having no experimental outcomes in common is called: a) Equally likely events e b) Exhaustive events c) Mutually exclusive events e d) Independent events 13. When the occurrence of one event has no effect on the probability of the occurrence of another event, the events are called: a) Independent e b) Dependent e c) Mutually exclusive e d) Equally likelye 14. The probability density function of a Markov process is a) p(x1,x2,x3..xn) = p(x1)p(x2/x1)p(x3/x2)...p(xn/xn-1)e b) p(x1,x2,x3.xn) = p(x1)p(x1/x2)p(x2/x3)..p(xn-1/xn)- c) p(x1,x2,x3..xn) = p(x1)p(x2)p(x3)..p(xn)e d) p(x1,x2,x3...xn) = p(x1)p(x2 *x1)p(x3*x2)...p(xn*xn-1)e ..xn) = ..... 15. The discrete probability distribution in which the outcome is very small with a very small period of time is classified as « a) Posterior distribution b) Cumulative distributione c) Normal distributione d) Poisson distributione 16. In a Poisson Distribution, the mean and variance are equal. a) True b) False
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