Proving Variance of t Distribution

In summary, the t distribution is a probability distribution used in statistical analysis to estimate population parameters with smaller sample sizes or unknown population standard deviations. Its importance lies in its ability to make inferences about a population using a sample. The variance of the t distribution is calculated by dividing the sample standard deviation by the square root of the sample size. The degrees of freedom for the t distribution is calculated as n-1, representing the number of independent pieces of information used to estimate a population parameter. As the sample size increases, the t distribution approaches the standard normal distribution. Proving the variance of the t distribution is significant because it helps us understand the variability of sample means and make accurate inferences about a population, determine appropriate sample sizes, and assess
  • #1
DavidLiew
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How to prove the variance of the t distribution?
 
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  • #2
DavidLiew said:
How to prove the variance of the t distribution?

I'm not sure "proof" is the proper concept here since the variance of the t distribution is defined as the second moment of the pdf as for all distributions. The variance is not defined for n=1,2. For values of n>2 the variance is derived from Wallis integrals discussed here in Tutorial 2:

http://www.aiaccess.net/English/Glossaries/GlosMod/e_gm_t_distri.htm#Tut_2_Variance

http://129.81.170.14/~vhm/papers_html/wrsf.pdf
 
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1. What is the t distribution and why is it important in statistics?

The t distribution is a probability distribution that is used in statistical analysis to estimate population parameters when the sample size is small or when the population standard deviation is unknown. It is important because it allows us to make inferences about a population using a sample, which is often more feasible and cost-effective than collecting data from an entire population.

2. How is the variance of the t distribution calculated?

The variance of the t distribution is calculated by dividing the sample standard deviation by the square root of the sample size. This is denoted as s²/n, where s is the sample standard deviation and n is the sample size.

3. What is the formula for calculating the degrees of freedom for the t distribution?

The degrees of freedom for the t distribution is calculated as n-1, where n is the sample size. This represents the number of independent pieces of information used to estimate a population parameter.

4. How is the t distribution related to the standard normal distribution?

The t distribution is related to the standard normal distribution in that as the sample size increases, the t distribution approaches the standard normal distribution. This is known as the central limit theorem, which states that the sampling distribution of the sample mean will be approximately normal regardless of the shape of the population distribution, as long as the sample size is large enough.

5. What is the significance of proving the variance of the t distribution?

Proving the variance of the t distribution is important because it allows us to understand the variability of sample means and make accurate inferences about a population. It also helps us to determine the appropriate sample size for a given study and to assess the reliability of our results.

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