Analyzing the Foundation of Conditional Logic
We start with the raw data: P(X∣Y)=21, P(Y∣X)=31, and P(X∩Y)=61.
The definition of conditional probability is our most powerful tool. It serves as the bridge between the intersection and the individual event.
We know that:
P(X∣Y)=P(Y)P(X∩Y)
By rearranging this, we find that
P(Y)=P(X∣Y)P(X∩Y). Substituting our values, we get:
P(Y)=1/21/6=31
We apply the same logic to
P(X) using
P(Y∣X)=P(X)P(X∩Y), which gives us:
P(X)=1/31/6=21
We have successfully mapped the individual probabilities of our two events.
The Union and the Test of Independence
Now that we know
P(X)=21 and
P(Y)=31, we can tackle the union. The
Addition Theorem of Probability is a beautiful piece of geometry:
P(X∪Y)=P(X)+P(Y)−P(X∩Y)
We subtract the intersection because when we add the two circles X and Y, the overlapping region X∩Y is counted twice. Subtracting it once restores the balance.
Plugging in our values:
P(X∪Y)=21+31−61=63+62−61=64=32
This confirms that Option A is correct.
Next, we face the ultimate question: Are X and Y independent? Independence is a profound concept—it means the occurrence of one event provides absolutely no information about the occurrence of the other.
Mathematically, this is defined by the condition:
P(X∩Y)=P(X)⋅P(Y)
Let us test it:
P(X)⋅P(Y)=21×31=61
Since this matches our given P(X∩Y)=61, we have proven that X and Y are indeed independent. Consequently, Option B is correct, and Option C is logically discarded.
The Final Verification
Finally, let us look at P(Xc∩Y). This represents the probability that Y occurs but X does not.
Geometrically, this is the part of
Y that lies outside of
X. We calculate this as:
P(Xc∩Y)=P(Y)−P(X∩Y)=31−61=61
Since $\frac{1}{6}
eq \frac{1}{3}$, we see that Option D is incorrect. Through this process, we have understood the mechanics of how events interact.