irections. From your family members, form a group of five. (Just in case your
family is composed of less than five members, you can add one from your
neighbors). Get the weight in the kilogram of each member of the group. Draw
random samples of size π = 2 from these weights.
1. List all possible samples and compute the mean of each sample.
2. Construct the sampling distribution of the sample means.
3. Find the mean of the population π.
4. Find the standard deviation of the population π.
5. Find the mean of the sampling distribution of the sample means ππΜ .
6. Find the standard deviation of the sampling distribution of the sample
means ππΜ .
7. Verify the Central Limit Theorem by:
a. Comparing π and ππΜ .
b. Comparing π and ππΜ .
Solution
Population size "N=5"
Weights obtained "=52,61,68,78,84"
Sample size "n=2"
1. Possible samples
"=C_5^2= \\binom{5}{2}=10" samples
"~~~~\\def\\arraystretch{1.5}\\begin{array}{c:c:c}no\n&Sample& Sample ~mean\\\\\\hline1&52,61&56.5\\\\\\hdashline2&52,68&60.0\\\\\\hdashline3&52,78&65.0\\\\\\hdashline4&52,84&68.0\\\\\\hdashline5&61,68&64.5\\\\\\hdashline6&61,78&69.5\\\\\\hdashline7&61,84&72.5\\\\\\hdashline8&68,78&73.0\\\\\\hdashline9&68,84&76.0\\\\\\hdashline10&78,84&81.0\\\\\\hline\\end{array}"
2. Sampling distribution of the sample means
"~~~~~\\def\\arraystretch{1.5}\\begin{array}{c:c:c}mean&freq& prob\\\\\\hline56.5&1&1\/10\\\\\\hdashline60.0&1&1\/10\\\\\\hdashline64.5&1&1\/10\\\\\\hdashline65.0&1&1\/10\\\\\\hdashline68.0&1&1\/10\\\\\\hdashline69.5&1&1\/10\\\\\\hdashline72.5&1&1\/10\\\\\\hdashline73.0&1&1\/10\\\\\\hdashline76.0&1&1\/10\\\\\\hdashline81.0&1&1\/10\\\\\\hline\\end{array}"
3. Mean of the population.
"\\mu=\\dfrac{\\sum X_i}{N}"
"\\mu=\\dfrac{52+61+68+78+84}{5}"
"\\mu =68.6"
4. Standard deviation
"\\sigma=\\sqrt{\\dfrac{\\sum (X_i-\\bar X)^2}{N}}"
"~~~\\def\\arraystretch{1.5}\\begin{array}{c:c:c}X_i&X_i-\\bar X&(X_i-\\bar X)^2\\\\\\hline56.5&-12.1&146.41\\\\\\hdashline60.0&-8.6&73.96\\\\\\hdashline64.5&-4.1&16.81\\\\\\hdashline65.0&-3.6&12.96\\\\\\hdashline68.0&-0.6&0.36\\\\\\hdashline69.5&0.9&0.81\\\\\\hdashline72.5&3.9&15.21\\\\\\hdashline73.0&4.4&19.36\\\\\\hdashline76.0&7.4&54.76\\\\\\hdashline81.0&12.4&153.76\\\\\\hline\\sum&&494.40\\\\\\hline\\end{array}"
"\\sigma=\\sqrt{\\dfrac{494.40}{5}}=9.944"
5. Mean of the sampling distribution
"\\mu_x=\\sum XP(X)"
"\\mu_x=56.5(0.1)+60.0(0.1)+64.5(0.1)+65.0(0.1)+68.0(0.1)+69.5(0.1)+72.5(0.1)+73.0(0.1)+76.0(0.1)+81.0(0.1)"
"\\mu_x=68.6"
6. Standard deviation of the sampling distribution of the sample means.
"\\sigma_x=\\dfrac{\\sigma}{\\sqrt N}=\\dfrac{9.944}{\\sqrt 5}"
"\\sigma_x=4.45"
7. Verification
The central limit theory states that if you take samples of larger and larger size from any population, then the mean "\\overline{x}" of the sample tends to get closer and closer to "\u03bc" and the standard deviation gets smaller.
(a) "\\mu=\\mu_{\\overline x} ~~~(68.6=68.6)"
hence verified.
(b) "\\sigma >\\sigma_{\\overline x}~~~(9.944>4.45)"
hence verified.
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