Statistical Probability Distributions

In summary, the conversation discusses two questions regarding joint probability distributions and finding the probability distribution of certain variables. The first question involves finding the probability distribution of X1X2 and X1/X2, while the second question involves determining the probability density of Z=XY^2. The conversation also mentions the importance of checking the sum of all probabilities to ensure accuracy in calculations.
  • #1
Fenix
9
0
I have two questions, I've completed. I am partially sure that the answers I've obtained are correct, and all I really want is confirmation on whether they are correct or not. If not, what am I doing wrong?

Question 1:

If the joint probability distribution of X1 and X2 is given by:

F(X1,X2) = (X1X2)/36

Where X1 = 1,2,3, and X2 = 1,2,3

a) Find the probability distribution of X1X2.

Solution:

Y = X1X2
X2 = Y/X1
dX2/dY = 1/X1

g(X1,Y) = [X1(Y/X1)/36][|1/X1|] = Y/36X1, For X1=1,2,3, and Y>0

Therefore, h(y) = Integral from 1 to 3: (Y/36X1)dX1 = Yln3/36, for Y>0

h(y) = 0, elsewhere.

b) Find the probability distribution of X1/X2

Solution:

Y = X1/X2
X2 = X1/Y
dX2/dY = -X1/Y^2

g(X1,Y) = [[X1(X1/Y)]/36][|-X1/Y^2|] = X1^3/36Y^3, For X1 = 1,2,3, and Y>0

Therefore, h(Y) = Integral from 1 to 3: (X1^3/36Y^3)dX1 = 5/9Y^3, for Y>0

h(y) = 0, elsewhere

Question 2:

Consider two random variables X and Y with the joint probability density:

f(X,Y) = {12XY(1-Y), for 0<X<1, 0<y<1.
0, elsewhere

Find the probability density of Z=XY^2 to determine the joint probability density of Y and Z and then integrating out Y.

Solution:

Z = XY^2
X = Z/Y^2
dX/dZ = 1/Y^2

g(Y,Z) = 12(Z/Y^2)(Y)(|1/Y^2|) = 12Z/Y^3, for 0<Y<1, and 0<Z<1

h(y) = Integral from 0 to 1: (12Z/Y^3)dy = Infinity, does not exist.


-----------------

References: Functions of Random Variables - Transformation Technique of Several Variables.

Thanks in advance. I appreciate it.
 
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  • #2
Fenix said:
I have two questions, I've completed. I am partially sure that the answers I've obtained are correct, and all I really want is confirmation on whether they are correct or not. If not, what am I doing wrong?

Question 1:

If the joint probability distribution of X1 and X2 is given by:

F(X1,X2) = (X1X2)/36

Where X1 = 1,2,3, and X2 = 1,2,3

a) Find the probability distribution of X1X2.

Solution:

Y = X1X2
X2 = Y/X1
dX2/dY = 1/X1

g(X1,Y) = [X1(Y/X1)/36][|1/X1|] = Y/36X1, For X1=1,2,3, and Y>0

Therefore, h(y) = Integral from 1 to 3: (Y/36X1)dX1 = Yln3/36, for Y>0

h(y) = 0, elsewhere.

These are discrete variables, why do you integrate?

Make tables for probabilities P(X1=i, X2=j) for i, j = 1, 2, 3. For example, P(X1=2, X2=3)=(2*3)/36 = 1/6. You get the same probability when X1=3 and X2=2: P(X1=3, X2=2)=1/6.

Now find the possible values for X1*X2, and the possible values of both X1 and X2 which yield that product.

X1*X2 can have the value of 6 either with X1=2 and X2=3 or with X1=3 and X2=2.

So P(X1X2=6)=P(2,3)+P(3,2)=2*1/6=1/3.

Try to proceed this way.

ehild
 
  • #3
You're right, that was a careless mistake.

I've corrected it now.

Instead of integration for Part a)

It should now read:

a)

h(y) = Sigma from X1=1 to 3: (Y/36X1) = Y/36+Y/72+Y/108 = 11Y/216, Y>0
h(y) = 0, elsewhere.

b)

h(y) = Sigma from X1=1 to 3: (X1^3/36Y^3) = (1^3+2^3+3^3)/36Y^3
= 36/36Y^3 = 1/Y^3, Y>0
h(y) = 0, elsewhere.

So how about now? Any other errors?

Also, what about the second question? Doesn't it strike you as odd that the answer is infinity?...
 
  • #4
Fenix said:
You're right, that was a careless mistake.

I've corrected it now.


I am afraid you did it entirely wrong. The sum of h(Y)-s for all possible values of x1*x2 which are 1, 2, 3, 4, 6, 9, must be 1. You can check yourself if your values meet this requirement. They do not.

You got h(Y)=11Y/216. The sum for all possible Y-s is 25*11/216 instead of 1.

Now again.

Y=1 can be only if both X1 and X2 is 1. f(X1,X2)=(X1X2)/36=1/36, so h(1)=1/36.

Y=2 is possible if either X1=1 and X2=2 or X1=2 and X2=1. f(1,2)=f(2,1)=2/36.
h(2)=f(1,2)+f(2,1)= 4/36.

Y=3 is obtained by x1=1 and x2=3 or vice versa. f(1,3)=f(3,1)=3/36, h(3)=6/36.

Yo get in the same way that h(4)= f(2*2) = 4/36;h(6)=f(2.3)+f(3,2)=12/36; h(9)=f(3,3)=9/36.

The sum of all h-s is (1+4+6+12+9)/36 =1.

Continue with question b...

ehild
 

FAQ: Statistical Probability Distributions

What is a statistical probability distribution?

A statistical probability distribution is a function that describes the likelihood of different outcomes occurring in a statistical experiment. It shows the range of possible values and how likely each value is to occur.

What are the types of statistical probability distributions?

There are several types of statistical probability distributions, including normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each type has its own characteristics and is used to model different types of data.

How are statistical probability distributions used in data analysis?

Statistical probability distributions are used in data analysis to help understand and visualize the data. They can also be used to make predictions and estimate the likelihood of certain outcomes occurring. Additionally, they provide important information for statistical tests and hypothesis testing.

What is the difference between discrete and continuous probability distributions?

Discrete probability distributions are used for data that can only take on certain values, such as counts or whole numbers. Continuous probability distributions are used for data that can take on any value within a range, such as measurements or time.

How do you determine the shape of a probability distribution?

The shape of a probability distribution is determined by its mean, median, and standard deviation. If the mean and median are equal and the standard deviation is small, the distribution will be symmetrical. If the mean and median are different and/or the standard deviation is large, the distribution will be skewed.

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