Answer to Question #334010 in Statistics and Probability for Lhay

Question #334010

Random sample of n=2 are drawn from a finite population consisting of the numbers 5,6,7,8,and 9

A.Find the mean population

B.find the standard deviation of the population

C. Find the mean of the sampling distribution of the sample means.

D.FIND The standard deviation of the sampling distribution of the sample means

E.Verify the central limit theorem



1
Expert's answer
2022-04-27T14:29:59-0400

A. We have population values 5,6,7,8,9, population size N=5 and sample size n=2.

Mean of population "(\\mu)" = "\\dfrac{5+6+7+8+9}{5}=7"

B. Variance of population 


"\\sigma^2=\\dfrac{\\Sigma(x_i-\\bar{x})^2}{n}=\\dfrac{1}{5}(4+1+0+1+4)"


"=2"

"\\sigma=\\sqrt{\\sigma^2}=\\sqrt{2}\\approx1.4142"

C. The number of possible samples which can be drawn without replacement is "^{N}C_n=^{5}C_2=10."

"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c}\n no & Sample & Sample \\\\\n& & mean\\ (\\bar{x})\n\\\\ \\hline\n 1 & 5,6 & 11\/2 \\\\\n \\hdashline\n 2 & 5,7 & 12\/2 \\\\\n \\hdashline\n 3 & 5,8 & 13\/2\\\\\n \\hdashline\n 4 & 5,9 & 14\/2 \\\\\n \\hdashline\n 5 & 6,7 & 13\/2 \\\\\n \\hdashline\n 6 & 6,8 & 14\/2 \\\\\n \\hdashline\n 7 & 6,9 & 15\/2 \\\\\n \\hdashline\n 8 & 7, 8 & 15\/2 \\\\\n \\hdashline\n 9 & 7,9 & 16\/2 \\\\\n \\hdashline\n 10 & 8,9 & 17\/2 \\\\\n \\hdashline \n\\end{array}"




"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c}\n \\bar{X} & f(\\bar{X}) &\\bar{X} f(\\bar{X}) & \\bar{X}^2f(\\bar{X})\n\\\\ \\hline\n 11\/2 & 1\/10 & 11\/20 & 121\/40 \\\\\n \\hdashline\n 12\/2 & 1\/10& 12\/20 & 144\/40 \\\\\n \\hdashline\n 13\/2 & 2\/10 & 26\/20 & 338\/40 \\\\\n \\hdashline\n 14\/2 & 2\/10 & 28\/20 & 392\/40 \\\\\n \\hdashline\n 15\/2 & 2\/10 & 30\/20 & 450\/40 \\\\\n \\hdashline\n 16\/2 & 1\/10 & 16\/20 & 256\/40 \\\\\n \\hdashline\n 17\/2 & 1\/10 & 17\/20 & 89\/40 \\\\\n \\hdashline\n\\end{array}"


Mean of sampling distribution 

"\\mu_{\\bar{X}}=E(\\bar{X})=\\sum\\bar{X}_if(\\bar{X}_i)=7=\\mu"



D. The variance of sampling distribution 

"Var(\\bar{X})=\\sigma^2_{\\bar{X}}=\\sum\\bar{X}_i^2f(\\bar{X}_i)-\\big[\\sum\\bar{X}_if(\\bar{X}_i)\\big]^2""=\\dfrac{1990}{40}-(7)^2=\\dfrac{3}{4}= \\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})"

"\\sigma_{\\bar{X}}=\\sqrt{\\dfrac{3}{4}}=\\dfrac{\\sqrt{3}}{2}\\approx0.8660"

E.


"\\mu_{\\bar{X}}=E(\\bar{X})=7=\\mu"


"\\dfrac{\\sigma^2}{n}(\\dfrac{N-n}{N-1})= \\dfrac{2}{2}(\\dfrac{5-2}{5-1})=\\dfrac{3}{4}=\\sigma^2_{\\bar{X}}"


Need a fast expert's response?

Submit order

and get a quick answer at the best price

for any assignment or question with DETAILED EXPLANATIONS!

Comments

No comments. Be the first!

Leave a comment

LATEST TUTORIALS
New on Blog
APPROVED BY CLIENTS