Finding the CDF: Solving for c & Understanding Results

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In summary, the conversation discusses calculating the CDF and PDF of a given function, specifically the probability density function of $c e^{-|x|}$. The correct approach is to first find the value of $c$ by setting the integral of the PDF to 1, and then calculating the definite integral to find the CDF. For part b), there may be a theorem relating the CDF of a transformed random variable to the CDF of the original random variable.
  • #1
nacho-man
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Please refer to the attached image.

For part a)

when I want to find the CDF, don't I simply take the indefinite integral of e^-|x|, multiply it by c and solve for that = 1?

I am unsure of how to take the integral for this, am i correct in saying it is -e^-x, for all x ?

that would leave me with c* -e^-x = 1, and c = 1/(-e^-x), wolfram says there are two separate results, but i am not sure why. and in that case, I would also have two separate results for c. how can that make sense?

for part b) i am unsure as to how to approach the question. could someone please guide me?

As always, your help is very much appreciated and invaluable.

Thanks
 

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  • #2
Hello nacho,
for part a), that's not what you are meant to do. Remember that probability density functions must integrate to 1 over the entire domain, that is:

$$\int_{-\infty}^{+\infty} c e^{- |x|} ~ \mathrm{d} x = 1$$

Can you solve for $c$ now? (you don't need to know what a CDF is for this part of the question)

Next, the CDF is by nature a definite integral, since when you say "the CDF of $f_X$ is $F(x)$" then you are saying that:

$$F(x) = \int_{- \infty}^x f_X (t) ~ \mathrm{d} t$$

Note the variable $t$, it is not the same as $x$. Basically summing up the PDF from negative infinity to $x$, the CDF variable. So in your case:

$$F(x) = \int_{-\infty}^x c e^{- |t|} ~ \mathrm{dt}$$

Which will give you an expression for the CDF in terms of $x$, and then finding its mean and variance should be straightforward.



For part b), I am not sure why you are asked to find the CDF first instead of the PDF, but there is probably some sort of theorem that relates the CDF of the transform of a random variable with the CDF of that random variable (which you found previously). Do your notes say anything?
 
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  • #3
Bacterius said:
Hello nacho,
for part a), that's not what you are meant to do. Remember that probability density functions must integrate to 1 over the entire domain, that is:

$$\int_{-\infty}^{+\infty} c e^{- |x|} = 1$$

Can you solve for $c$ now? (you don't need to know what a CDF is for this part of the question)


For part b), I am not sure why you are asked to find the CDF first instead of the PDF, but there is probably some sort of theorem that relates the CDF of the transform of a random variable with the CDF of that random variable (which you found previously). Do your notes say anything?

Thanks

What I wanted to clarify was, for part a)
this is what I did:

split up the integral to:

$$ C(int\e^x dx + \int e(^-x)dx) = 1 $$

which gives

$$ c = 1/{e^x-e^(-x)} $$
Is this correct?


god I'm bad at latex.

What i meant was c(int e^x dx + int e^-x dx) = 1
thus c = 1/(e^x -e^(-x)) ?looking at my notes for part b) I can't seem to find any relationship.
 
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  • #4
No, it's not correct. You cannot find $c$ in terms of $x$, that doesn't make sense. Remember what I mentioned earlier: a PDF always has an area (under its curve) of $1$. That means that if you integrate your PDF from negative infinity to positive infinity and you get, say, $2c$, then you know that $2c = 1$ and so $c = \frac{1}{2}$. Basically the fact that the probability density function must integrate to $1$ puts conditions of what values $c$ can take (and, in fact, it can only take one value).

So it's a definite integral. Can you integrate $f(x) = c e^{-|x|}$ from negative infinity to positive infinity (find the area under this function's curve, in terms of $c$)? What do you get?

Rereading your post you are pretty close, but again you are not calculating a definite integral but only an indefinite one. You want to calculate:

$$\int_{-\infty}^{+\infty} c e^{-|x|} ~ \mathrm{d} x = 1$$

And you correctly identified that you should split it for $x < 0$ and $x > 0$. So:

$$\int_{-\infty}^{+\infty} c e^{-|x|} ~ \mathrm{d} x = \int_{-\infty}^{0} c e^{x} ~ \mathrm{d} x + \int_{0}^{+\infty} c e^{-x} ~ \mathrm{d} x = 1$$

I think this is what you were missing. Can you complete it now?

You are mixing up definite and indefinite integrals.
 
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  • #5
ahhh,
thank you
that makes sense now, I see what you mean.

I was having trouble with the bounds when i split it up, so I thought it was just supposed to calculate the indefinite integral.

i understand now though!
 

Related to Finding the CDF: Solving for c & Understanding Results

1) What is the CDF and why is it important in scientific research?

The CDF, or cumulative distribution function, is a mathematical function that represents the probability distribution of a random variable. It helps to understand the likelihood of a particular outcome occurring and is commonly used in statistical analysis to make predictions and draw conclusions.

2) How do you solve for c in a CDF?

To solve for c in a CDF, you need to use the inverse function of the CDF, known as the quantile function. This function takes a probability value as input and outputs the corresponding value of the random variable. By setting the probability value to 1, you can find the maximum value of the random variable (c) in the CDF.

3) What do the results of a CDF tell us?

The results of a CDF show the cumulative probability of a random variable being less than or equal to a specific value. This can be useful in determining the likelihood of a certain event occurring or understanding the distribution of data.

4) What are some common applications of finding the CDF?

Finding the CDF is commonly used in fields such as statistics, economics, engineering, and finance. It can be used to analyze data, make predictions, and understand the likelihood of certain events occurring.

5) How can understanding the CDF benefit scientific research?

Understanding the CDF can benefit scientific research by providing a deeper understanding of the distribution of data and the likelihood of certain outcomes. It can also help in making accurate predictions and drawing conclusions from data. Additionally, the CDF can be used to compare different datasets and determine if they follow similar distributions.

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