Need Help with Mean and Variance Calculations for Distributions?

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In summary, the webpage provides a way to derive the mean of a binomial distribution and the variance of a binomial distribution. It also provides a way to solve for the variance of a binomial distribution.
  • #1
Dr.Brain
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Ok I am stuck up deriving the 'variance for Binomial Distribution' and mean for the 'Hypergeometric distribution '

For variance part , I first derived that variance can be written as =(second moment about origin) - (square of mean)

But I am having trouble calaculating the second moment about the origin .Please can sum1 tell me sum site which can help me?
 
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  • #2
to get the "second moment about the mean" you need to sum i2P(i) for i= 0 to n. For the binomial distribution, with probabilities p, 1-p, P(i)= nCipi(1-p)n-i. That is, you are summing
[tex]\Sum_{i=0}^n _nC_i i^2 p^i (1-p)^{n-i}[/tex]
Can you relate that to the binomial theorem?
 
  • #3
HallsofIvy said:
to get the "second moment about the mean" you need to sum i2P(i) for i= 0 to n. For the binomial distribution, with probabilities p, 1-p, P(i)= nCipi(1-p)n-i. That is, you are summing
[tex]\Sum_{i=0}^n _nC_i i^2 p^i (1-p)^{n-i}[/tex]
Can you relate that to the binomial theorem?

thats where I am stuck , I don't know how to solve this binomial further , its been a long time since I did Binomial, maybe lack of practice..
 
  • #4
please sum1 help.
 
  • #5
Look at this:
http://www.bbc.co.uk/education/asguru/maths/14statistics/03binomialdistribution/12meanandvariance/index.shtml
 
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  • #6
ok I read it , that's a good way to prove the mean of Binomial Distribution , but I want the proof for variance of Binomial , I want to solve it the same way as I told above.
 
  • #7
Then keep reading! The first half of the page gives a very simple way of deriving the mean (Since one trial the value is either 0 or 1, the mean is 0*(1-p)+ 1(p)= p. Since trials are independent, the mean of n trials is the sum of the means of each: np) the second half of the page derives the variance in the same way.
 
  • #8
ok thanks , I got hold of that idea.

One more thing , can u pls tell me how ot solve this thing:

[tex]\Sum_{i=0}^n _nC_i i^2 p^i (1-p)^{n-i}[/tex]

I am interested to know this.!
 
  • #9
More detail please?

Hi, I think that web page is too trivial. Do you know a more detail page? Thanks!
 

FAQ: Need Help with Mean and Variance Calculations for Distributions?

What is the mean?

The mean, also known as the average, is a measure of central tendency that represents the sum of all values in a dataset divided by the total number of values. It is commonly used to describe the typical value in a dataset.

What is the variance?

The variance is a measure of spread or variability in a dataset. It is calculated by taking the average of the squared differences between each value and the mean. A higher variance indicates a wider range of values, while a lower variance indicates a more clustered set of values.

How do you calculate the mean?

To calculate the mean, you add all the values in a dataset and then divide by the total number of values. The formula for calculating the mean is: mean = sum of all values / total number of values.

How do you calculate the variance?

To calculate the variance, you first need to calculate the mean. Then, for each value in the dataset, subtract the mean and square the result. Next, add all the squared differences and divide by the total number of values. The formula for calculating the variance is: variance = sum of (value - mean)^2 / total number of values.

Why are mean and variance important in statistics?

Mean and variance are important statistical measures because they provide valuable information about the central tendency and variability of a dataset. They can help us understand the typical value in a dataset, as well as how spread out the data points are from the mean. These measures are often used in hypothesis testing, data analysis, and decision making in various fields of science and research.

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