Probability and Smoking Risks

The video tutorial explains how to determine conditional probability using a table and Bayes' theorem. It uses an example involving life insurance policyholders, where 4% are smokers. The tutorial calculates the probability that a policyholder who died was a smoker, first using a table and then Bayes' theorem. The results are verified to be consistent across both methods, demonstrating the application of conditional probability in real-world scenarios.

What is the probability of dying during the year for a non-smoker?

• 0.04

• 0.96

• 0.05

• 0.014

How many policyholders are assumed in the example to simplify calculations?

• 100,000

• 200,000

• 10,000

• 50,000

What is the total number of smokers expected to die during the year?

• 3,800

• 200

• 1,344

• 4,000

How many non-smokers are expected to live during the year?

• 96,000

• 3,800

• 1,344

• 94,656

What is the probability that a policyholder who died was a smoker, rounded to four decimal places?

• 0.1295

• 0.0140

• 0.0500

• 0.9600

In Bayes Theorem, what does event A represent in this context?

• Policyholder survived

• Policyholder was a non-smoker

• Policyholder died

• Policyholder was a smoker

What is the probability of a policyholder being a smoker?

• 0.05

• 0.014

• 0.96

• 0.04

What is the complement of being a smoker in this example?

• Being alive

• Being a non-smoker

• Being deceased

• Being a smoker

What is the probability of a non-smoker dying during the year?

• 0.014

• 0.05

• 0.96

• 0.04

What method can be used to verify the probability calculation besides Bayes Theorem?

• Using a graph

• Using a list

• Using a chart

• Using a table