Independent identically distributed random variables

In summary, when discussing two independent and identically distributed random variables with exponential distributions, it is important to note that they must have the same lambda value. This means that not only do they both have an exponential distribution, but all aspects of their distributions are identical. While Poisson Random Variables can have different lambda parameters, this does not apply when discussing identically distributed variables.
  • #1
Somefantastik
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0
For two independent and identically distributed random variables having the exponential distribution, do they have the same lambda value, or are the lambda values different?
 
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  • #2
If the have the same distribution function, then they both must use the same lambda.
 
  • #3
I just wasn't sure b/c it looks like Poisson Random Variables can have different lambda parameters.
 
  • #4
That is true.
 
  • #5
Yes, they can, but your original post said "identically distributed". That doesn't mean only that the both have an exponential distribution but that they have the same exponential distribution. If they are "identically distributed" then everything about the distributions is the same.
 

1. What is the concept of "independent identically distributed random variables"?

Independent identically distributed (iid) random variables are a set of variables that are statistically independent and have the same probability distribution. This means that the outcome of one variable does not affect the outcome of another, and each variable has the same likelihood of occurring.

2. How are independent identically distributed random variables used in statistics and data analysis?

Iid random variables are commonly used in hypothesis testing and statistical modeling. They allow for simplification of complex data sets and make it easier to draw conclusions and make predictions.

3. What is the significance of the "identically distributed" aspect of iid random variables?

The identically distributed aspect ensures that all variables have the same probability distribution, making it easier to compare and analyze them. This is important in statistical analysis as it allows for fair and accurate comparisons.

4. How are independent identically distributed random variables generated or obtained?

Iid random variables can be generated through simulations or obtained from real-world data sets. They can also be mathematically defined using specific probability distributions, such as the normal distribution.

5. Are there any assumptions or limitations when using independent identically distributed random variables?

One of the main assumptions when using iid random variables is that they are truly independent and identically distributed. In real-world data, this may not always be the case, which can lead to biased or inaccurate results. Additionally, depending on the specific probability distribution used, the variables may have certain limitations and may not accurately represent the entire population.

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