"n_1=n_2=16 \\\\\n\n\\bar{x_1}=40000 \\\\\n\ns_1 = 5400 \\\\\n\n\\bar{x_2}= 38009 \\\\\n\ns_2=3200 \\\\\n\nH_0: \\mu_1 \\leq \\mu_2 \\\\\n\nH_1: \\mu_ 1> \\mu_2"
When n1 and n2 are small and "\\sigma_1" and "\\sigma_2" are unknown, we have to use the two-sample t test for independent random samples from two normal populations having the same unknown variance.
Test-statistic:
"t= \\frac{\\bar{x_1} - \\bar{x_2}}{s_p \\sqrt{(1\/n_1) + (1\/n_2)}} \\\\\n\n\ns^2_p= \\frac{(n_1-1)s^2_1 +(n_2-1)s^2_2}{n_1+n_2-2} \\\\\n\n\ns^2_p= \\frac{(16-1)(5400)^2 +(16-1)(3200)^2}{16+16-2} \\\\\n\n\n= \\frac{4.374 \\times 10^8 + 1.536 \\times 10^8}{30} \\\\\n\n\n= 0.197 \\times 10^8 \\\\\n\n\ns_p=0.4438 \\times 10^4 \\\\\n\n\n= 4438.4 \\\\\n\nt= \\frac{40000 -38009}{4438.4 \\sqrt{(1\/16) + (1\/16)}} \\\\\n\n\n= \\frac{1991}{1569.2} \\\\\n\n\n= 1.268"
Reject H0 if "t\u2265t_{crit}"
Let use α=0.05
For one-tail test and degree of freedom "df = n_1+n_2-2= 30, \\; t_{crit} = 1.697,"
"t=1.268 < t_{crit}= 1.697."
Accept H0 at 0.05 significance level.
Therefore, there is enough evidence to claim that the population mean μ1 is less than the population mean μ2. Brand D is better than C.
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