What Is the Distribution of 2(X̄ - 10) / S?

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SUMMARY

The distribution of 2(X̄ - 10) / S, where X̄ is the sample mean and S is the sample standard deviation, is derived from the properties of normal distributions. In the context of independent and identically distributed random variables with mean zero and variance 36, the sequence Cn is determined by the limit of the probability distribution converging to the standard normal distribution. The ratio of independent chi-squared variables follows a Beta distribution, specifically Beta2(n1/2, n2/2). The mean squared error (MSE) of theta-hat approaches zero as n approaches infinity, confirming the consistency of theta-hat.

PREREQUISITES
  • Understanding of normal distribution and its properties
  • Knowledge of sample mean (X̄) and sample standard deviation (S)
  • Familiarity with chi-squared distributions and their degrees of freedom
  • Basic concepts of convergence in probability and limit theorems
NEXT STEPS
  • Study the Central Limit Theorem and its implications for sample means
  • Learn about the Beta distribution and its relationship to chi-squared variables
  • Explore the derivation of the F distribution from chi-squared distributions
  • Investigate the properties of consistent estimators and their MSE behavior
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Statisticians, data analysts, and students studying probability theory and statistical inference will benefit from this discussion.

buddingscientist
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1. If X1, X2, X3, X4 are a random samle from a normal distribution with mean 17, then what is the distribution of [itex]2(X - 10) / S[/itex] where X should be X bar.

Our notes are just awful for this topic.
Any tips how to proceed with this one, and what is S?


2. Let X1, X2, ... be a sequence of independent and identically distributed random variables with mean zero and variance 36. What is the sequece Cn for which

lim p -> inf [itex]P ( (X / Cn) \leq x) = P (Z \leq x), Z ~ N(0,1) ?[/itex]


Would this just be sqrt(Var) = sqrt(36) = 6 ?


4. What is the distribution of X/Y where X and Y are independent [itex]{X^{2}}_{1}[/itex] random variables? (chi-squared with 1 deg. of freedom)

Not sure how to progress with this one, do we play with the degrees of freedom to get some sort of trivial answer?



5. Is theta-hat consistent if MSE(theta-hat) = [itex]e^{1/n} -1[/itex].

Since MSE approaches 0 as n approaches infinity (n-> inf, MSE -> [itex]e^{0} -1[/itex] -> 0) then yes, theta-hat is consistent. ?


Thanks for your time
 
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1. S is most likely to be standard deviation
2. I have no idea what this question is about
3.
There is a theorem which states that
"if X1 and X2 are two indepdendent chi-squared variates with n1 and n2 d.f resp., then X1/X2 is a Beta2(n1/2,n2/2)"
Direct use of this theorem makes this one easy. I am not sure if u are aware of this theorem. If u want i can prove this one.

-- AI
 
I think you need to know the standard deviation of X before you can answer 1. S is probably the sample standard deviation, defined as the sum over all i of (Xi - Xbar)/(n - 1).

Your notation on 2 is confusing. I believe you meant: lim p --> inf (P(Xp/Cp < x) = P(Z < x)) Assuming you meant that, it's probably intended that X is distributed normally. If it is distributed normally, then all you need to do is know how to transform a normal distribution with mean 0 into a standard normal distribution.

For 4, you can also use the F distribution. If X1 and X2 are 2 independent RV's distributed as chi-squares with a and b degrees of freedom respectively, then (X1 / a) / (X2 / b) has the F distribution with a and b degrees of freedom.

I can't help you with 5.
 

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