Okay I’m not sure how to answer this but I think they do it by law
E and F are two events and that P(E)=0.3 and P(F|E)=0.5. Thus, P(E and F)=0.15
Bayes' theorem is transforming preceding probabilities into succeeding 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.
P(F|E)=P(E and F)÷P(E)
It is given that P(E)=0.3,P(F|E)=0.5
Using Bayes' formula,
P(F|E)=P(E and F)÷P(E)
Rearranging the formula,
⇒P(E and F)=P(F|E)×P(E)
Substituting the given values in the formula, we get
⇒P(E and F)=0.5×0.3
⇒P(E and F)=0.15
∴The correct answer is 0.15.
If, E and F are two events and that P(E)=0.3 and P(F|E)=0.5. Thus, P(E and F)=0.15.
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Answer:
This is an example of causation because the patient that took the medication recovered quicker than the patient that did not.
Step-by-step explanation:
Correlation is a statistical technique that shows that a relation exists between 2 variables but does not give any explanation about the relationship
Example: Correlation between Air Conditioner sales and sunglasses sold.
As the sales of the air conditioner is increasing so do the sales of sunglasses.
Causation is a statistical technique that shows a relation ship between 2 variable. It emphasizes the fact that any change in the value of one variable will cause a change in the value of another variable.
(2+4+6)*8 hopefully that's what you are looking for
