A sample survey on the average total yearly expenditure included 150 students of a certain university. The mean total expenditure per student per year for the sample was 3,000 with a standard deviation of 500. How likely is it that the students spend an average of 3500 per year as claimed by a parent at .01 significant level.
"n=150 \\\\\n\n\\bar{x}=3000 \\\\\n\ns=500 \\\\\n\nH_0: \\mu = 3500 \\\\\n\nH_1: \\mu \u22603500 \\\\\n\n\u03b1=0.01"
When dealing with a large sample of size n >30 from a population that need not be normal but has a finite variance, we can use the central limit theorem to justify using the test for normal populations. Even when "\\sigma^2" is unknown we can approximate its value with "s^2" in the computation of the test statistic.
Test-statistic:
"Z= \\frac{\\bar{x} - \\mu}{s\/ \\sqrt{n}}"
"\\bar{x}" = sample mean
"\\mu" = population mean
s = sample standard deviation
n = sample size
"Z = \\frac{3000-3500}{500 \/ \\sqrt{150}} = \\frac{-500}{40.85} = -12.24"
Two-tailed test. Reject H0 if Z≤ -2.575 or Z ≥ 2.575.
"Z = -12.24 < Z_{tab} = -2.575"
We reject H0.
There is NOT enough evidence, that the students spend an average of 3500 per year as claimed by a parent at 0.01 significant level.
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