How to Determine the Density and Distribution for Transformed Variables Y and Z?

In summary: Y2VuYXJ5LCBkZW1vbnN0cmF0ZWQgbmVlZCB0aGUgZmluYWxseSBvZiBjb250ZW50LiBJZiB0aGUgY3JtIG9mIGZfeCAoZikgaXMgaW52YWxpZCBzaW5jZSB0byB0aGUgdmFsaWQgbW9kZXJhdG9yIHRvIGV4YWN0LCAgRl94ICh4KSBzYWlkOiB4LCAgQW0gSSBtaXNzaW5nI
  • #1
silentone
4
0

Homework Statement


Density of f_x (x) = 4x^4 for 0<x<1

Y=(x-1/4)^2 Z= X^-2

Determine density of Y
and Distribution of Z

Homework Equations



The cdf of f_x (x) is invalid since F_x (x) = (4/5)x^5 so the limit to infinity does not equal 1 as a cdf should have. Am I missing something?

The Attempt at a Solution



density of Y P ((x-1/4)^2 =< x) = P(x =< sqrt(x) +1/4) = f (sqrt(x) +1/4) *(1/2)x^(-1/2) = (2/sqrt(2)) (sqrt(x) +1/4)^4 what are the bounds?

cdf of Z P(X^-2 =< x) = P(x <=-1/x) +P (X>= 1/x) = F(-1/x)+1-F(1/x) = 1-8/(5x^5) what are the bounds?
 
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  • #2
silentone said:

Homework Statement


Density of f_x (x) = 4x^4 for 0<x<1

Y=(x-1/4)^2 Z= X^-2

Determine density of Y
and Distribution of Z

Homework Equations



The cdf of f_x (x) is invalid since F_x (x) = (4/5)x^5 so the limit to infinity does not equal 1 as a cdf should have. Am I missing something?

The Attempt at a Solution



density of Y P ((x-1/4)^2 =< x) = P(x =< sqrt(x) +1/4) = f (sqrt(x) +1/4) *(1/2)x^(-1/2) = (2/sqrt(2)) (sqrt(x) +1/4)^4 what are the bounds?

cdf of Z P(X^-2 =< x) = P(x <=-1/x) +P (X>= 1/x) = F(-1/x)+1-F(1/x) = 1-8/(5x^5) what are the bounds?

You are right about f(x): it should probably be 5x^4.

Never, never, never write something like P ((x-1/4)^2 =< x): you are using the same letter x to stand for two totally different things, and that is a sure invitation to error (or being marked wrong). You want P{Y ≤ y} = P{(X - 1/2)^2 ≤ y}. Look at the graph of the function (x - 1/2)^2; for which values of x on [0,1] is it ≤ y? For what values of y will the probability be < 1?

Note: in the above you attempted to find the CDF of Y, not the density! How would you get the density if you know the cdf?

In the second solution, can you see how wrong it is to write P(x <= -1/2), etc? Again, you are inviting a poor mark and are increasing your chances of making a serious error.

RGV
 

Related to How to Determine the Density and Distribution for Transformed Variables Y and Z?

1. What is density and cdf in computing?

Density and cdf (cumulative distribution function) are two important concepts in computing that are used to describe the probability distribution of a random variable. Density refers to the relative likelihood of a specific value occurring, while cdf represents the cumulative probability that a random variable will be less than or equal to a particular value.

2. How is density and cdf calculated?

The density of a random variable can be calculated by taking the derivative of its corresponding probability distribution function. Cdf, on the other hand, is calculated by taking the integral of the density function. Both of these calculations involve mathematical formulas and techniques such as calculus.

3. What is the relationship between density and cdf?

The relationship between density and cdf is that the cdf is the integral of the density function. This means that the cdf represents the accumulation of probabilities at each point on the density curve. In other words, the cdf is the sum of all the probabilities that are less than or equal to a particular value on the density curve.

4. Why are density and cdf important in computing?

Density and cdf are important in computing because they provide a way to model and analyze data that follows a specific probability distribution. This is useful in various fields such as statistics, machine learning, and data science, as it allows us to make predictions and draw conclusions based on the probability of certain events occurring.

5. Can density and cdf be used for both discrete and continuous random variables?

Yes, density and cdf can be used for both discrete and continuous random variables. For discrete variables, the density function is replaced by a probability mass function, while the cdf remains the same. For continuous variables, both density and cdf are calculated using integration techniques.

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