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.
We need to construct the 95% confidence interval for the population mean "\\mu." The following information is provided:
"\\bar{X}=12.68, \\sigma=6.83, n=50,"
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(12.68-1.96\\times{6.83\\over\\sqrt{50}},12.68+1.96\\times{6.83\\over\\sqrt{50}}\\big)\\approx"
"\\approx(10.787, 14.573)"
Therefore, based on the data provided, the 95% confidence interval for the population mean is "10.787<\\mu<14.573," which indicates that we are 95% confident that the true population mean "\\mu"
is contained by the interval "(10.787, 14.573)."
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