A set of events is said to be exhaustive if, between them, they cover all possible outcomes.
Read the introduction to probability and probability measures at StatLect.com
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Dependent probability is the probability of an event which changes according to the outcome of some other event.
It is the probability of an event calculated from repeated trials of an experiment.
Probability/ Statistics
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No, it is not. Furthermore, with ordinary phones, it is not even possible.
A sets events is said to be exhaustive if the performance of the experiment always results in the accurance of atleast one of them.
Mutually exhaustive refers to a set of outcomes or events in which all possible scenarios are accounted for, ensuring that at least one of the outcomes must occur. In other words, when events are mutually exhaustive, they cover the entire sample space, leaving no possibility unconsidered. This concept is often used in probability and statistics to ensure comprehensive analysis of events. For example, the outcomes of flipping a coin (heads or tails) are mutually exhaustive.
From a probability perspective fair means equal probability.
Read the introduction to probability and probability measures at StatLect.com
It means multiply, Probaility of A and B means probability of A multiplied by probability of B.
The mean of a binomial probability distribution can be determined by multiplying the sample size times the probability of success.
The probability level for an outcome is the probability that the outcome was at least as extreme as the one that was observed.
The significance of the mean of a probability distribution is that it is the most probably thing to happen. The mean is the average of a set of values. If it is the average of a probability distribution, it is the most probable part.
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Dependent probability is the probability of an event which changes according to the outcome of some other event.