Bayes' theorem provides a way to transform prior probabilities into posterior probabilities. Group of answer choices True False

Mathematics

1 answer:

Dvinal [7]3 years ago

6 0

Answer:

True

Step-by-step explanation:

Bayes' theorem is indeed a way of transforming prior probabilities into posterior probabilities. It is based on the principle of conditional probability. Conditional probability is the possibility that an event will occur because it is dependent on another event.

The prior probability in this theorem is the present understanding we possess about the possible outcome of an event based on the current understanding we have about the subject. Posterior probability on the other hand is the new understanding we have of the subject matter based on an experiment that has just been performed on it. Bayes' Theorem finds widespread application which includes the fields of science and finance. In the finance world, for example, Bayes' theorem is used to determine the probability of a debt being repaid by a debtor.

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When using substitution to solve this system of equations, what is the result of the first step x=6y+3

myrzilka [38]

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Choose the missing step in the given solution to the inequality −6x − 10 > 14 + 2x. −6x − 10 > 14 + 2x _______________ −8x

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-2x -2x

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Tomtit [17]

Answer:

34.01% probability that his score is at least 532.1.

Step-by-step explanation:

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

In this problem, we have that:

$\mu = 493, \sigma = 95$