"\\bar{x_2}=185.625, s_2=50.2449"
"\\bar{x_3}=162.50, s_3=29.6226"
The following null and alternative hypotheses need to be tested:
"H_0:\\sigma_1^2=\\sigma_3^2"
"H_1:\\sigma_1^2\\not=\\sigma_3^2"
This corresponds to a two-tailed test, for which a F-test for two population variances needs to be used.
Based on the information provided, the significance level is "\\alpha=0.1," and the the rejection region for this two-tailed test is "R=\\{F:F<0.252\\ or\\ F>4.876\\}."
The F-statistic is computed as follows:
Since from the sample information we get that "F_L=0.252<0.923=F<4.876=F_R," it is then concluded that the null hypothesis is not rejected.
Therefore, there is not enough evidence to claim that the population variance "\\sigma_1^2" is different than the population variance "\\sigma_3^2," at the "\\alpha=0.1" significance level.
The following null and alternative hypotheses need to be tested:
"H_0:\\mu_1=\\mu_3"
"H_1:\\mu_1\\not=\\mu_3"
This corresponds to a two-tailed test, for which a t-test for two population means, with two independent samples, with unknown population standard deviations will be used.
Based on the information provided, the significance level is "\\alpha=0.1," and the degrees of freedom are "df=12." In fact, the degrees of freedom are computed as follows, assuming that the population variances are equal:
Hence, it is found that the critical value for this two-tailed test is "t_c=1.782," for "\\alpha=0.1" and"df=12."
The rejection region for this two-tailed test is "R=\\{t:|t|>1.782\\}." .
Since it is assumed that the population variances are equal, the t-statistic is computed as follows:
Since it is observed that "|t|=4.168>1.782=t_c," it is then concluded that the null hypothesis is rejected.
Therefore, there is enough evidence to claim that population mean "\\mu_1" is different than "\\mu_3," at the 0.1 significance level.
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