How to Derive the PDF of a Continuous Random Variable?

In summary: A1 - Yes, that is correct. You simply substitute the value of 2-y into the cdf equation to get the cdf for Y.A2 - This step is possible because of the properties of a cdf. The cdf represents the probability that the random variable is less than or equal to a given value. So, subtracting this probability from 1 will give the probability that the random variable is greater than that value. This is why we can substitute the cdf of X (F) with 1-F(2-y) to get the cdf of Y.
  • #1
mcfc
17
0
Hello

I'm not too sure if this is the correct location for my post, but it's the best fit I can see!

The cdf of the continuous random variable X is
[tex]F(x)=\left\{\begin{array}{cc}0&\mbox{ if }x< 0\\
{1\over 4} x^2 & \mbox{ if } 0 \leq x \leq 2\\
1 &\mbox{ if } x >2\end{array}\right.[/tex]

Q1-Obtain the pdf of X
Q2-If Y = 2 - X, derive the pdf of the random variable Y

A1-I think the cdf is given by [tex]f(x) = F'(x)=\left\{\begin{array}{cc}{1\over 2}x &\mbox{ if } 0 \leq x \leq 2 \\
0 &\mbox{ elsewhere } \end{array}\right.[/tex]
Is that correct?

A2-For the pdf of Y: [tex]G(Y) = P(Y \leq y) = P(2 - x \leq y) = P(x \geq 2-y)
[/tex] but I'm not sure how to proceed??
Thanks
 
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  • #2
A1 - you're right.

A2 [tex]G(y) = P(Y \leq y) = P(2 - X \leq y) = P(X \geq 2-y) = 1-P(X < 2-y)=1-F(2-y)[/tex]
G'(y)=F'(2-y) for the pdf
 
  • #3
mathman said:
A1 - you're right.

A2 [tex]G(y) = P(Y \leq y) = P(2 - X \leq y) = P(X \geq 2-y) = 1-P(X < 2-y)=1-F(2-y)[/tex]
G'(y)=F'(2-y) for the pdf

Thanks,
Q1-how would I evaluate:
[tex]G(y) = 1-F(2-y)\
\mbox{to give the cdf? Do I just substitute 2-y into }{1 \over 4} x^2 \mbox{ to give }
G(Y) = 1 - {1 \over 4} (2-y)^2 = 4y - {1 \over 4}y^2\ for \ 0 \leq y \leq 2[/tex]
[tex] \mbox{for the cdf of Y? and so for the pdf: } 4-{1 \over 2}y[/tex]

Q2-Why are you able to make this step: [tex]1-P(X < 2-y)=1-F(2-y)[/tex]
 

FAQ: How to Derive the PDF of a Continuous Random Variable?

What is distribution in statistics?

Distribution in statistics refers to the way in which data is spread out or distributed. It describes the pattern of values or outcomes that a variable can take on and how often those values occur.

Why is it important to find the distribution of data?

Finding the distribution of data can help us understand the characteristics of a dataset and make more informed decisions. It can also help us identify any outliers or patterns in the data, which can be useful for making predictions or drawing conclusions.

How do you determine the distribution of data?

Determining the distribution of data involves analyzing the shape of the data, such as whether it is symmetrical or skewed, and calculating summary statistics like mean, median, and mode. Graphical tools like histograms and box plots can also be used to visualize the distribution.

What are the different types of distributions?

There are many different types of distributions, but some common ones include normal (bell-shaped), uniform, binomial, and exponential. The type of distribution often depends on the nature of the data and the underlying processes that generate it.

How is the distribution of a dataset affected by outliers?

Outliers can significantly impact the distribution of a dataset, especially if they are extreme values. They can skew the data and make it difficult to determine the true pattern or characteristics of the data. It is important to identify and address outliers before analyzing the distribution of a dataset.

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