Joint Probability FunctionStatistical Independence

In summary, the conversation discusses the concept of statistical independence and its relation to the functions g(x) and h(y). The attempted solution uses the definition of independence to show that g(x) and h(y) are not equal, while the given solution uses a sketch to determine the correct upper bound for the integral in h(y).
  • #1
Saladsamurai
3,020
7

Homework Statement


Picture1-45.png



Homework Equations



If X and Y are statistically independent, then f(x,y) = g(x)h(y) where
[itex]g(x) = \int f(x,y) dy[/itex]
[itex]h(y) = \int f(x,y) dx[/itex]



The Attempt at a Solution



(a)
[itex]g(x) = \int f(x,y) dy = \int_{y=0}^{1-x} 6x\, dy[/itex]
[itex]\Rightarrow g(x)=6x(1-x)[/itex]

and

[itex]h(y) = \int f(x,y) dx = \int_{x=0}^{1} 6x \,dx[/itex]
[itex]\Rightarrow h(y)=3[/itex]

Thus h(y)g(x) [itex]\ne[/itex] f(x,y) and thus X and Y are NOT statistically independent.

Now before I move onto (b) look at the solution that the text gives.

Picture2-26.png


I have no idea what is going on in the upper bound for the h(y) integral? They also went a different route with the solution, but I think that my way should work since it is a definition of independence. But clearly our h(y) functions should be the same. What am I missing?

Thanks,
Casey
 
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  • #2
If you sketch the region over which f(x,y)=6x, you'll see it's a triangle with vertices at (0,0), (0,1), and (1,0). From the sketch, you can see that for a given value of y, f(x,y)=6x only for values of x between 0 and 1-y; therefore, the upper limit of the integral should be 1-y, not 1.
 
  • #3
Excellent! I should have sketched it. Thanks Vela. :smile:
 
  • #4
Saladsamurai said:
Excellent! I should have sketched it.
I can't think of any situations where this isn't good to do.
 

FAQ: Joint Probability FunctionStatistical Independence

What is a joint probability function?

A joint probability function is a mathematical function that assigns a probability to each possible combination of values for two or more random variables. It describes the likelihood of events occurring simultaneously.

How is a joint probability function calculated?

A joint probability function is calculated by taking the product of the individual probabilities for each event. For example, if the probability of event A is 0.5 and the probability of event B is 0.3, the joint probability of both events occurring is 0.5 x 0.3 = 0.15.

What is statistical independence?

Statistical independence refers to two or more events that do not influence each other. In other words, the outcome of one event has no effect on the outcome of the other event.

How is statistical independence related to joint probability functions?

If two events are statistically independent, their joint probability function can be calculated by simply multiplying the individual probabilities. This is because the events are not affected by each other and can be treated as separate, unrelated events.

Why is statistical independence important in probability and statistics?

Statistical independence allows for simpler and more accurate calculations of probabilities, as well as providing a more accurate representation of real-world situations. It also allows for the use of certain statistical methods and models that require independence assumptions.

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