Answer is C
hope this helps!
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.
Going off the idea that this is 4/(y+2) - 9/(y-2) = 9/(y^2-4), let's first rewrite y^2-4 in the form of a^2-b^2, where a=y and b=2.
y/(y+2)-9/(y-2)=9/(y^2-2^2)
Now, use difference of squares: a^2-b^2=(a+b)(a-b)
4/(y+2)-9/(y-2)=9/((y+2)(y-2))
Multiply both sides by the Least Common Denominator: (y+2)(y-2)
4(y-2)-9(y+2)=9
Then keep on simplifying until you get your answer
-5y-26=9
-5y=9+26
-5y=35
y=35/-5
y=-7
Yay! Our answer is y=-7!
Answer:
The 99% confidence interval for the mean commute time of all commuters in Washington D.C. area is (22.35, 33.59).
Step-by-step explanation:
The (1 - <em>α</em>) % confidence interval for population mean (<em>μ</em>) is:
Here the population standard deviation (σ) is not provided. So the confidence interval would be computed using the <em>t</em>-distribution.
The (1 - <em>α</em>) % confidence interval for population mean (<em>μ</em>) using the <em>t</em>-distribution is:
Given:
*Use the <em>t</em>-table for the critical value.
Compute the 99% confidence interval as follows:
Thus, the 99% confidence interval for the mean commute time of all commuters in Washington D.C. area is (22.35, 33.59).
