Expected value of function in binomial distribution

In summary, you shifted from x to y in step 5 but forgot to adjust the lower bound of the summation. You need to compensate for the term you lost.Thanks, but doesn't the summation still equates to 1?The regular expectation of a binomial distribution with parameters n and p is given by:EX = npThis means that:sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1 = EY = (n+1)pApart from that we have a first term for which must be
  • #1
kwy
17
0
Hi members,

Hope someone can help with this assignment question? I need to proof:
E(1/1+X) = [1-(1-p)^n+1]/p(n+1) where X ~ Bi(n,p)

Below are my steps and I'm not sure where I went wrong:
1. sum(x=0 to n) (1/1+x)*(n choose x)*p^x*(1-p)^n-x
2. sum(x=0 to n) (1/1+x)*[n!/x!*(n-x)!]*p^x*(1-p)^n-x
3. sum(x=0 to n) [n!/(x+1)!*(n-x)!]*p^x*(1-p)^n-x
4. sum(x=0 to n) [n!/(x+1)!*(n-x)!]*[(n+1)/(n+1)]*p^x*(p/p)*(1-p)^n-x
5. 1/p(n+1) * sum(x=0 to n) [(n+1)!/(x+1)!*(n-x)!]*p^x+1*(1-p)^n-x
let y = x+1
6. 1/p(n+1) * sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1

Think now the whole summation part equates to 1, leaving me with 1/p(n+1) only.

Any help will be much appreciated. Thanks in advance.
 
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  • #2
When you shifted from x to y you forgot to adjust the lower bound of the summation.
You need to compensate for the term you lost.
 
  • #3
Thanks, but doesn't the summation still equates to 1?

6. 1/p(n+1) * sum(y=1 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1

Which would still leave me with 1/p(n+1) instead of [1-(1-p)^n+1]/p(n+1)?
 
  • #4
kwy said:
Thanks, but doesn't the summation still equates to 1?

6. 1/p(n+1) * sum(y=1 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1

Which would still leave me with 1/p(n+1) instead of [1-(1-p)^n+1]/p(n+1)?

The regular expectation of a binomial distribution with parameters n and p is given by:
EX = np

This means that:
sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1 = EY = (n+1)p

Apart from that we have a first term for which must be compensated.
This would be:
(1-p)^(n+1)

Can you put those together?
 
  • #5
Oh dear, if EY is p(n+1), I think I somehow got the entire equation to equal to 1. I believe I went off the wrong track after line 3. Would you agree? Sorry, it's been close to 20 years since I've done this kind of maths. My brain just cannot interpret things as fast. I'm really appreciating your assistance. Thanks.
 
  • #6
kwy said:
Hope someone can help with this assignment question? I need to proof:
E(1/1+X) = [1-(1-p)^n+1]/p(n+1) where X ~ Bi(n,p)

Now that I look at your assignment question again, I think the question is wrong.
I suspect it should be:

[tex]E(\frac 1 {1+X}) = 1- \frac {(1-p)^{n+1}} {p(n+1)} \text{ where } X \sim Bi(n,p)}[/tex]

Can you confirm?

And as far as I can tell your calculation is entirely correct until step 6 where you added (1-p)n+1 without noticing.
 
  • #7
Hi I like Serena

The question on the sheet definitely says

1 - (1-p)^n+1
-----------------
p(n+1)

Cheers and thanks.
 
  • #8
kwy said:
Hi I like Serena

The question on the sheet definitely says

1 - (1-p)^n+1
-----------------
p(n+1)

Cheers and thanks.

My bad, the expectation EY is not in the picture.

You're quite right when you say:

sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1 = 1

This basically states that the sum of the chances on all possible outcomes is 1.

That still leaves the first term for which must be compensated.
More specifically:

1 = sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1 =
(1-p)^(n+1) + sum(y=1 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1
 
  • #9
I got it. You are right about line 6 where the summation's lower bound should y=1. This means the summation part is:

sum(y=1 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1

which equates to say A - B where

A = sum(y=0 to n+1) [(n+1)!/y!*(n-y+1)!]*p^y*(1-p)^n-y+1 = 1
B where y is 0 giving [(n+1)!/0!*(n-0+1)!]*p^0*(1-p)^n-0+1 = (1-p)^n+1

Many thanks for the reminder.

Cheers
 

1. What is the expected value of a function in binomial distribution?

The expected value of a function in binomial distribution is the average value that would be obtained from an infinite number of trials of the same experiment. It is calculated by multiplying the probability of success in each trial by the number of trials, and then adding all these values together.

2. How is the expected value of a function calculated in binomial distribution?

The expected value of a function in binomial distribution is calculated by multiplying the probability of success (p) by the number of trials (n), and then adding all these values together. The formula for expected value is E(x) = np, where n is the number of trials and p is the probability of success in each trial.

3. What is the relationship between expected value and binomial distribution?

Expected value is a key concept in binomial distribution, as it represents the average outcome of a binomial experiment. It is used to predict the most likely outcome of a series of trials, and is an important measure of central tendency for binomial distributions.

4. Can the expected value of a function in binomial distribution be negative?

No, the expected value of a function in binomial distribution cannot be negative. This is because the expected value is a measure of central tendency, and it is not possible to have a negative number of successes in a binomial experiment.

5. How is expected value different from actual outcome in binomial distribution?

The expected value of a function in binomial distribution is a predicted value based on the probability of success and the number of trials. The actual outcome, on the other hand, is the result obtained from a specific number of trials. While the expected value is a theoretical concept, the actual outcome is based on real data from the experiment.

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