The average expenditure per student (based on average daily attendance) for a certain school year was $10,337 with a population standard deviation of $1560. A survey for the next school year of 150 randomly selected students resulted in a sample mean of $10, 798. Find the 95% confidence level? Should the null hypothesis be rejected?
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:
"=(10798-1.96\\times\\dfrac{1560}{\\sqrt{150}}, 10798+1.96\\times\\dfrac{1560}{\\sqrt{150}})"
"=(10548.35, 11047.65)"
Therefore, based on the data provided, the 95% confidence interval for the population mean is "10548.35 < \\mu < 11047.65," which indicates that we are 95% confident that the true population mean "\\mu" is contained by the interval "(10548.35, 11047.65)."
The following null and alternative hypotheses need to be tested:
"H_0:\\mu=10337"
"H_1:\\mu\\not=10337"
This corresponds to a two-tailed test, for which a z-test for one mean, with known population standard deviation will be used.
Based on the information provided, the significance level is "\\alpha = 0.05," and the critical value for a two-tailed test is "z_c = 1.96."
The rejection region for this two-tailed test is "R = \\{z: |z| > 1.96\\}."
The z-statistic is computed as follows:
Since it is observed that "|z| = 3.619 > 1.96=z_c," it is then concluded that the null hypothesis is rejected.
Using the P-value approach:
The p-value is "p=2P(Z>3.6193)=0.000295," and since "p=0.000295<0.05=\\alpha," it is concluded that the null hypothesis is rejected.
Therefore, there is enough evidence to claim that the population mean "\\mu" is different than "10337," at the "\\alpha = 0.05" significance level.
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