If "n>30," the Central Limit Theorem can be used.
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, has approximately a normal distribution with "\\mu_{\\bar{X}}=\\mu" and "\\sigma_{\\bar{X}}=\\sigma^2\/n."
Give that "n=36, \\mu=70, \\sigma=6."
"n=36>30=>X\\sim(\\mu;\\sigma^2\/n)". Then
"P(70.5<\\bar{X}<71.5)=P(Z<{71.5-70\\over 6\/\\sqrt{36}})-P(Z<{70.5-70\\over 6\/\\sqrt{36}})="
"=P(Z<{1.5})-P(Z<0.5)\\approx0.93319-0.69146\\approx0.2417"
The probability that the sample mean is between "70.5" and "71.5" is "0.2417."
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