Since the chosen sample sizes "n_1" and "n_2" are greater than 30 and the population variances are known, we consider using the Normal distribution to perform our test.
The hypothesis we test is,
"H_0:\\mu_1=\\mu_2"
"Against"
"H_1:\\mu_1\\gt\\mu_2"
The test statistic is given by the formula,
"Z^*=(\\bar{x}_1-\\bar{x}_2)\/\\sqrt{(\\sigma_1^2\/n_1+\\sigma_2^2\/n_2)}"
"Z^*=(13585-13600)\/\\sqrt{1024\/51+1024\/34}=-15\/7.084919=-2.1172(4\\space d\\space p)."
"Z^*" is compared with the table value at "\\alpha=0.01" level of significance and this value is given by, "Z^*=2.33" and reject the null hypothesis if "Z^*\\gt Z_\\alpha"
Since "Z^*=-2.1172\\lt Z_\\alpha=2.33" , we fail to reject the null hypothesis and we conclude that there is no sufficient evidence to show that on average the houses in town B are cheaper than the one in town A at 1% level of significance.
To compute the p-value, we use the formula below.
"p-value=1-\\phi(Z^*)". We use this formula because the test we are performing is an upper-one tailed test.
First, "\\phi(Z^*)=\\phi(-2.1172)" is obtained in normal distribution table.
Thus, "\\phi(-2.1172)=0.0170" and the p-value is 1-0.0170=0.983.
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