Conditional Probability & Bayes' Theorem

In summary, the problem discusses three stores with varying numbers of employees and percentages of women. The solution uses these numbers to calculate the probability of a woman resigning from each store and the probability of the woman working in store C. The final calculation uses the given information to determine the likelihood of the woman working in store C.
  • #1
Somefantastik
230
0
[Problem]
Stores A, B, and C have 50, 75, and 100 employees, respectively, 50, 60, and 70 percent of these are women. Resignations are equally likely among all employees, regardless of sex. One employee resigns and this is a woman. What is the prob. she works in store C?

[Solution]
Store A: 25F
Store B: 45F
Store C: 70F

P(W|A) = 25/50 (Prob. it was a woman resign given store A)
P(W|B) = 45/75
P(W|C) = 70/100


P(A) = P(B) = P(C) = 1/3 ?

or

P(A) = 50/225
P(B) = 75/225
P(C) = 100/225 ?


P(C|W) = [tex]\frac{P(W|C)P(C)}{P(W|C)P(C) + P(W|A)P(A) + P(W|B)P(B)}[/tex]

Does this look right?
 
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  • #2
P(A) = 50/225
P(B) = 75/225
P(C) = 100/225

P(C|W) = P(C and W)/P(W) = P(W|C)P(C)/[itex]\sum_s[/itex]P(W|s)P(s), so it's correct.
 
  • #3
Thank you very much.
 

1. What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is denoted as P(A|B), where A is the event of interest and B is the event that has already occurred.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the intersection of the two events (P(A∩B)) by the probability of the condition event (P(B)). This can be written as P(A|B) = P(A∩B) / P(B).

3. What is Bayes' Theorem?

Bayes' Theorem is a mathematical formula that provides a way to calculate the conditional probability of an event based on prior knowledge or information. It is written as P(A|B) = (P(B|A) * P(A)) / P(B), where P(A) and P(B) are the individual probabilities of events A and B, and P(B|A) is the conditional probability of event B given that event A has occurred.

4. How is Bayes' Theorem used in real-life situations?

Bayes' Theorem is commonly used in fields such as statistics, data analysis, and machine learning to make predictions and decisions based on available information. It is also used in medical diagnosis, weather forecasting, and financial forecasting.

5. What are some limitations of Bayes' Theorem?

Some limitations of Bayes' Theorem include the assumption that the prior probabilities are accurate and that the events are independent. In real-life situations, these assumptions may not always hold true, leading to inaccurate predictions. Additionally, Bayes' Theorem relies on the availability of accurate and relevant data, which may not always be the case.

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