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A sampling distribution is a distribution of chance statistics that can be obtained from multiple samples drawn from a distinct population. It is the distribution of frequencies of a wide range of possible outcomes for a statistic of a population. A statistical population is a whole pool from where we draw a statistical sample. It may also refer to a group of events, people, objects, etc. The sampling distribution of the mean is the average weight calculated for each sample set in the sampling distribution.
The Bayes’ theorem is a formula in mathematics that is used to determine conditional probability. We can define conditional probability as the chance of an outcome happening based on the occurrence of the previous outcome. The Bayes’ rule provides analysts with a method of revising existing theories or predictions given additional or new evidence. The Bayes’ rule is applied by financial institutions to compute the risk of loaning money to potential borrowers.
A joint probability is the likelihood of two events occurring simultaneously. These two events are usually designated as A and B. It is also known as the probability of the intersection of two or multiple events. A Venn diagram is often used to represent this intersection. A joint probability distribution highlights the likelihood distribution for two or multiple random variables. The main aim of a joint distribution is to find associations between two variables.