The Central Limit Theorem
Let "X_1,X_2,...,X_n" be a random sample from a distribution with mean "\\mu" and variance "\\sigma^2." Then if "n"
is sufficiently large, "\\bar{X}" has approximately a normal distribution with "\\mu_{\\bar{X}}=\\mu" and "\\sigma_{\\bar{X}}^2=\\sigma^2\/n."
If "n>30" the Central Limit Theorem can be used.
The following information is provided: "\\bar{X}=72, \\sigma=10, n=150."
We need to construct the 95% confidence interval for the population mean "\\mu."
The critical value for "\\alpha=0.05" is "z_c=z_{1-\\alpha\/2}=1.96." The corresponding confidence interval is computed as shown below:
"=\\big(72-1.96\\times{10\\over\\sqrt{150}},72+1.96\\times{10\\over\\sqrt{150}}\\big)\\approx"
"\\approx(70.400, 73.600)"
We need to construct the 99.5% confidence interval for the population mean "\\mu."
The critical value for "\\alpha=0.005" is "z_c=z_{1-\\alpha\/2}=2.807."
The corresponding confidence interval is computed as shown below:
"=\\big(72-2.807\\times{10\\over\\sqrt{150}},72+2.807\\times{10\\over\\sqrt{150}}\\big)\\approx"
"\\approx(69.708, 74.292)"
(b) How many samples must be taken to get a 99.5% CI for averages in "\\pm1.5" seconds from the actual price?
"n\\geq\\bigg({z_c\\sigma\\over1.5}\\bigg)^2"
"n\\geq\\bigg({2.807\\cdot10\\over1.5}\\bigg)^2"
"n\\geq351"
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