**Conditional Probability Calculation**In an experiment, we have events A and B with the following probabilities:- Probability of event A, $ P(A) = 0.6 $- Probability of event B, $ P(B) = 0.7 $We also have the probability of the intersection of the complement of A and the complement of B:- $ P(A^c \cap B^c) = 0.1 $Based on this information, we need to compute the conditional probability $ P(B | A^c) $. The available choices are:- $ 0.60 $- $ 0.75 $- $ 0.80 $- $ 0.34 $

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Description: **Conditional Probability Calculation**In an experiment, we have events A and B with the following probabilities:- Probability of event A, $ P(A) = 0.6 $- Probability of event B, $ P(B) = 0.7 $We also have the probability of the intersection of

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An experiment can result in events A with probability, P(A) =0.6 and event B with probability, P(B) = 0.7. Assuming that P(A^nB©) = 0.1, compute the following probabilities: P(B| A°) is

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Concept explainers

Contingency Table

A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.

Binomial Distribution

Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.

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**Conditional Probability Calculation**

In an experiment, we have events A and B with the following probabilities:
- Probability of event A, $ P(A) = 0.6 $
- Probability of event B, $ P(B) = 0.7 $

We also have the probability of the intersection of the complement of A and the complement of B:
- $ P(A^c \cap B^c) = 0.1 $

Based on this information, we need to compute the conditional probability $ P(B | A^c) $. The available choices are:
- $ 0.60 $
- $ 0.75 $
- $ 0.80 $
- $ 0.34 $

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