Construct a stem-and-leaf display for carbon monoxide per cigarette smoked. Check whether there is any outlier in the given data.

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3. Bayesian Inference – Updating Beliefs A medical test for a rare disease has the following characteristics: Sensitivity (true positive rate): 99% Specificity (true negative rate): 98% The disease occurs in 0.5% of the population. A patient receives a positive test result. Questions: a) Define the relevant events and use Bayes’ Theorem to compute the probability that the patient actually has the disease.b) Explain why the result might seem counterintuitive, despite the high sensitivity and specificity.c) Discuss how prior probabilities influence posterior beliefs in Bayesian inference.d) Suppose a second, independent test with the same accuracy is conducted and is also positive. Update the probability that the patient has the disease.

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Chapter 2.3, Problem 8P

To determine

Construct a stem-and-leaf display for carbon monoxide per cigarette smoked.

Check whether there is any outlier in the given data.

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