Probability - Exponential Distribution

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SUMMARY

The discussion focuses on deriving the probability density function (pdf) of the random variable Y defined as Y=(alpha)X, where X follows an Exponential distribution with parameter alpha. The steps outlined include finding the cumulative distribution function (cdf) of X, using the relationship between Y and X to derive the cdf of Y, and differentiating the cdf of Y to obtain its pdf. The pdf of X is given as f(x)=αe^{-αx} for x≥0, and the process involves integration and differentiation of these functions.

PREREQUISITES
  • Understanding of Exponential distribution and its properties
  • Knowledge of cumulative distribution functions (cdf) and probability density functions (pdf)
  • Familiarity with integration and differentiation in calculus
  • Basic concepts of random variables and their transformations
NEXT STEPS
  • Study the derivation of the pdf for other distributions, such as Normal and Poisson
  • Learn about transformations of random variables in probability theory
  • Explore applications of Exponential distribution in real-world scenarios
  • Investigate the use of software tools like R or Python for statistical analysis of distributions
USEFUL FOR

Statisticians, data scientists, and students in probability theory who are looking to deepen their understanding of Exponential distributions and their transformations.

Brains_Tom
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Here's the evil question:

Let X~Exponential(alpha). Derive and name the pdf of Y=(alpha)X
 
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Hi! You should show some of your thoughts or working in your post...

But anyway, here are some steps to guide you along.

Step 1: Find the cumulative distribution function (cdf) of X. Since X is continuous, you will need to integrate the pdf of X [tex](f(x)=\alpha e^{-\alpha x}, for x\geq 0)[/tex] , with the lower limit being 0 (since we define [tex]x\geq0[/tex] for an exponential distribution) and the upper limit an arbitrary constant x.

Step 2: Use the relation [tex]Y= \alpha X[/tex] to derive the cdf of Y from the cdf of X. So [tex]F(y) = P(Y\leq y) = P(\alpha X\leq y) = P(X\leq \frac{y}{\alpha})[/tex]

Step 3: We can calculate this final probability since we know the cdf of X.

Step 4: Finally, differentiate the cdf of Y to obtain its pdf.

You will get a nice answer in the end.

All the best!

Note: Letters in small casing (e.g. x, y) represent constants while block letters (e.g. X, Y) are used to define the random variables.
 
Last edited:

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