Depdendent, Independent, and Complementary Events

In summary: It is not possible to determine the independence from the usual sort of Venn diagram because you need quantitative information.
  • #1
PFuser1232
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20
For two events ##A## and ##B##, where ##A∩B ≠ ∅##, is it possible to deduce from a venn diagram whether or not those two events are dependent? Or is such information unattainable from a mere venn diagram? Also, if we define ##C## as the complement of the union of ##A## and ##B##, why must ##C## be dependent on ##A'##? Why is ##P(C)## not equal to ##P(C|A')##?
 
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  • #2
MohammedRady97 said:
For two events ##A## and ##B##, where ##A∩B ≠ ∅##, is it possible to deduce from a venn diagram whether or not those two events are dependent? Or is such information unattainable from a mere venn diagram?

It isn't possible to determine ithe independence from the usual sort of Venn diagram because you need quantitative information. If probability is represented on a scale proportional to area, you need to determine if the ratio of the area of [itex]A [/itex] to the total area is the same as the ratio of [itex] A \cap B [/itex] to the area of [itex] B [/itex].

Also, if we define ##C## as the complement of the union of ##A## and ##B##, why must ##C## be dependent on ##A'##? Why is ##P(C)## not equal to ##P(C|A')##?

What are you claiming? - "not always equal to" or "never equal to" ?
 
  • #3
If the Venn diagram is labeled - yes, you can determine independent or not - but with the same dull calculations you'd do if you were simply provided the numerical information itself.

Are you asking why, if [itex] C = A \cup B [/itex], that [itex] C' = A' \cap B' [/itex] ? If so, you can convince (not form a proof) yourself of why that is by looking at your hypothetical Venn Diagram. The set C consists of the entire region inside the regions that represent A and B: the complement of C is the entire region inside the diagram but outside the two regions for A and B: that region outside is [itex] A' \cap B' [/itex].

If that is what you meant - why did you pick on the complement of A alone?
 
  • #4
Stephen Tashi said:
It isn't possible to determine ithe independence from the usual sort of Venn diagram because you need quantitative information. If probability is represented on a scale proportional to area, you need to determine if the ratio of the area of [itex]A [/itex] to the total area is the same as the ratio of [itex] A \cap B [/itex] to the area of [itex] B [/itex].
What are you claiming? - "not always equal to" or "never equal to" ?

Never equal to.
 
  • #5
statdad said:
If the Venn diagram is labeled - yes, you can determine independent or not - but with the same dull calculations you'd do if you were simply provided the numerical information itself.

Are you asking why, if [itex] C = A \cup B [/itex], that [itex] C' = A' \cap B' [/itex] ? If so, you can convince (not form a proof) yourself of why that is by looking at your hypothetical Venn Diagram. The set C consists of the entire region inside the regions that represent A and B: the complement of C is the entire region inside the diagram but outside the two regions for A and B: that region outside is [itex] A' \cap B' [/itex].

If that is what you meant - why did you pick on the complement of A alone?

What I was asking was: why is the probability of event ##C## different from the probability of event ##C## given ##A'## (or ##B'## really). It does make sense to me; intuitively, since ##P(C|A') = \frac{P(C∩A')}{P(A')}##, and the intersection of ##C## and ##A'## is ##C##, it is clear that ##P(C)## and ##P(C|A')## differ by a factor of ##P(A')##.
But using this same logic on a venn diagram, can't we just as well come to the wrong conclusion that whenever there is an intersection between two events ##A## and ##B##, ##A## must always be dependent on ##B## since the occurrence of ##B## implies the occurrence of the intersection, which is also a part of ##A##?
 
  • #6
MohammedRady97 said:
Never equal to.

[itex] C [/itex] might be the null set.
 
  • #7
MohammedRady97 said:
For two events ##A## and ##B##, where ##A∩B ≠ ∅##, is it possible to deduce from a venn diagram whether or not those two events are dependent?
One way to visualize independent events in venn diagrams is shown in these two examples. The first shows 2 independent events. One is the horizontal blue area and the other is the vertical pink area. For independent events, the fraction of the horizontal area within the vertical area is the same as its fraction within the entire universal square. The second diagram shows dependent events. Most venn diagrams are just shown as blobs where the areas are not useful for drawing any conclusions.
venn_diagram_independent_events.png


Or is such information unattainable from a mere venn diagram? Also, if we define ##C## as the complement of the union of ##A## and ##B##, why must ##C## be dependent on ##A'##? Why is ##P(C)## not equal to ##P(C|A')##?
For C to be independent of A', it must have the same probability with or without A'. But that is the same as saying it has the same probability without A or with A. We know that P(C|A) = 0, so C and A' can not be independent.
 
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FAQ: Depdendent, Independent, and Complementary Events

What are dependent events?

Dependent events are events where the outcome of one event affects the outcome of another event. This means that the probability of the second event occurring changes based on the outcome of the first event.

What are independent events?

Independent events are events where the outcome of one event does not affect the outcome of another event. This means that the probability of the second event occurring remains the same regardless of the outcome of the first event.

What are complementary events?

Complementary events are events where the outcomes are mutually exclusive and together make up the entire sample space. This means that if one event occurs, the other event cannot occur and vice versa.

How do you calculate the probability of dependent events?

To calculate the probability of dependent events, you multiply the probabilities of each event occurring together. However, you also need to take into account the impact of the first event on the second event. This can be done by using conditional probability, which takes into account the given information from the first event.

How do you calculate the probability of independent events?

To calculate the probability of independent events, you multiply the probabilities of each event occurring together. However, since the events are independent, the outcome of one event does not affect the other event, so you do not need to use conditional probability.

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