Calculating E[x] for f(x)=e^-2|x| distribution in the reals (x e R)

In summary, we discussed calculating E[X] for a continuous distribution with density f(x)=e^-2|x| on the interval x e R. Some confusion arose regarding the appropriate integral bounds to use, but it was clarified that using -∞ and ∞ is correct due to the given interval. The calculations were then shown and it was determined that E[X]=0 due to the symmetry of the function and the adjustment for negative x in the integration.
  • #1
HappyN
16
0
I want to calculate E[x] of the following continuous distribution having density: f(x)=e^-2|x|
for x in the reals (x e R)

I did the calculation with integral bounds infinity and minus infinity, are these the right bounds to use since we are only told x e R?
I got 0 as the answer, can someone tell me if they get the same?
 
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  • #2
Hi HappyN! :smile:

I'm guessing that you didn't adjust the integral for negative x to take account of the |x| :wink:

(it usually makes the integral negative if you forget)
 
  • #3
do you mean my bounds are wrong?
i'm not quite sure of what you mean by adjusting the integral for negative x?
 
  • #4
Show us your full calculations. :smile:
 
  • #5
To calculate E[X] I did: ∫xf(x) dx (integral bounds between minus ∞ and ∞ - sorry don't know how to type it properly!)
using integration by parts, i got:
E[x]=[-x/2 e^-2|x|] + [1/4 e^-2|x|] (bounds evaluated between -∞ and ∞)
=(-∞/2 e^-2|∞|) - (∞/2 e^-2|∞|) + (-1/4 e^-2|∞| + 1/4 e^-2|∞|)
=-∞e^-2|∞|
which is 0?
therefore E[x]=0?
 
  • #6
It should be obvious by symmetry that E[x]= 0.
 
  • #7
Hi HappyN! :smile:

(just got up :zzz: …)
HappyN said:
E[x]=[-x/2 e^-2|x|] + [1/4 e^-2|x|] (bounds evaluated between -∞ and ∞)

(try using the X2 icon just above the Reply box :wink:)

if x < 0, then eg d/dx e-2|x| = d|x|/dx d/d|x| e-2|x|

= (-1) -2e-2|x|

the d|x|/dx makes everything negative for negative x ! :smile:
 

1. What is the formula for calculating expectation?

The formula for calculating expectation is E(x) = ∑xP(x), where x represents the value of each possible outcome and P(x) represents the probability of that outcome occurring.

2. What is the purpose of calculating expectation?

The purpose of calculating expectation is to determine the average value or outcome of a random variable over a large number of trials. It can help predict the most likely outcome and make informed decisions.

3. How do you interpret the result of an expectation calculation?

The result of an expectation calculation represents the long-term average value or outcome of a random variable. It is not a guarantee of what will happen in a single trial, but rather a prediction based on probability.

4. Can expectation be negative?

Yes, expectation can be negative. This means that the average value or outcome of a random variable is less than zero. It is important to consider both positive and negative expectations when making decisions based on probability.

5. What factors can affect the accuracy of an expectation calculation?

The accuracy of an expectation calculation can be affected by the sample size, the accuracy of the probabilities used, and any assumptions made about the random variable. It is important to have a large enough sample size and accurate probabilities to get a more reliable result.

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