Calculating Conditional Probabilities in Boolean Algebra

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In summary, it is found that $42\%$ of the people have never skied yet, that $58\%$ of them have never flown yet, and that $29\%$ of them have already flown and skied.
  • #1
mathmari
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MHB
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Hey! :eek:

I am looking the following concering boolean algebra.

For a certain test group, it is found that $42\%$ of the people have never skied yet, that $58\%$ of them have never flown yet, and that $29\%$ of them have already flown and skied.
Which probability is higher then:
To meet someone, who has already skied, from the group of those who have never flown, or to find someone who has already flown from the group of those who have already skied?

I have done the following:

Let $S$ be the event that someone has already skied and let $F$ be the event that someone has already flown.
Then it is given that $P(\overline{S})=42\%$, $P(\overline{F})=58\%$ and $P(S\land F)=29\%$, or not? (Wondering)

From these probabilities, we get also $P(S)=100\%-P(\overline{S})=100\%-42\%=58\%$ and $P(F)=100\%-P(\overline{F})=100\%-58\%=42\%$.

We are looking for the conditional probabilities $P(S\mid \overline{F})$ and $P(F\mid S)$, or have I understood that wrong? (Wondering)

We have that \begin{align*}&P(S\mid \overline{F})=\frac{P(S\land \overline{F})}{P(\overline{F})} \\ &P(F\mid S)=\frac{P(F\land S)}{P(S)}=\frac{29\%}{58\%}=50\%\end{align*}

How can we calculate $P(S\land \overline{F})$ ? (Wondering)
 
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  • #2
Hey mathmari! (Happy)

The easiest way is to make a Venn Diagram. Even if only to understand what we want to achieve.

The following Venn Diagram represents the problem doesn't it? (Thinking)
\begin{tikzpicture}
\begin{scope}[blend group = soft light]
\fill[red!30!white] (-1,0) circle (2);
\fill[blue!30!white] (1,0) circle (2);
\end{scope}
\node at (-1,2) {$S$};
\node at (1,2) {$F$};
\node at (-1.8,0) {$29\%$};
\node at (1.8,0) {$13\%$};
\node at (0,0) {$29\%$};
\end{tikzpicture}
We can immediately see that $P(S\land\bar F)=29\%$.

More formally, we have that $(S\land \bar F)$ and $(S\land F)$ have an empty intersection, and their union is $S$.
Therefore they are mutually exclusive and we can apply the sum rule:
$$P(S)=P\big((S\land F) \lor (S\land\bar F)\big) = P(S\land F) + P(S\land \bar F)
\implies P(S\land\bar F) = P(S)-P(S\land F)$$
This is effectively how the Venn Diagram was constructed in the first place. (Thinking)
 
  • #3
Klaas van Aarsen said:
The following Venn Diagram represents the problem doesn't it? (Thinking)
\begin{tikzpicture}
\begin{scope}[blend group = soft light]
\fill[red!30!white] (-1,0) circle (2);
\fill[blue!30!white] (1,0) circle (2);
\end{scope}
\node at (-1,2) {$S$};
\node at (1,2) {$F$};
\node at (-1.8,0) {$29\%$};
\node at (1.8,0) {$13\%$};
\node at (0,0) {$29\%$};
\end{tikzpicture}

Ahh yes! (Malthe)
Klaas van Aarsen said:
More formally, we have that $(S\land \bar F)$ and $(S\land F)$ have an empty intersection, and their union is $S$.
Therefore they are mutually exclusive and we can apply the sum rule:
$$P(S)=P\big((S\land F) \lor (S\land\bar F)\big) = P(S\land F) + P(S\land\bar F)
\implies P(S\land\bar F) = P(S)-P(S\land\bar F)$$
This is effectively how the Venn Diagram was constructed in the first place. (Thinking)

Ahh ok, I see! So, we have that $$P(S\land\bar F) = P(S)-P(S\land F)=58\%-29\%=29\%$$

Therefore, the probability to meet someone, who has already skied, from the group of those who have never flown is equal to $$P(S\mid \overline{F})=\frac{P(S\land \overline{F})}{P(\overline{F})}=\frac{29\%}{58\%}=50\%$$ and the probability to find someone who has already flown from the group of those who have already skied is equal to $$P(F\mid S)=\frac{P(F\land S)}{P(S)}=\frac{29\%}{58\%}=50\%$$ So, these two probabilities are equal. Is everything correct? (Wondering)
 
  • #4
It looks good to me. (Nod)
 
  • #5
Klaas van Aarsen said:
It looks good to me. (Nod)

Great! Thanks a lot! (Yes)
 

1. What is the difference between theoretical probability and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data collected from experiments or observations.

2. How do you calculate the probability of an event?

To calculate the probability of an event, you divide the number of favorable outcomes by the total number of possible outcomes. This gives you a decimal value, which can be expressed as a percentage.

3. Can the probability of an event be greater than 1?

No, the probability of an event cannot be greater than 1. A probability of 1 means that the event is certain to occur, while a probability of 0 means that the event is impossible.

4. What is the difference between independent and dependent events?

Independent events are events where the outcome of one event does not affect the outcome of another event. Dependent events are events where the outcome of one event does affect the outcome of another event.

5. How does the sample size affect the probability of an event?

The larger the sample size, the more accurate the calculated probability will be. This is because a larger sample size provides a better representation of the population and reduces the impact of random chance.

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