Answer:
Step-by-step explanation:
The claim here is that the brand name aspirin is more consistent in the amount of active ingredient used than the generic aspirin.
This is a test of 2 independent groups. The population standard deviations are not known. Let μ1 be the mean amount of active ingredients in brand name aspirin and μ2 be the mean amount of active ingredients in generic name aspirin
The random variable is μ1 - μ2 = difference in the mean amount of active ingredients between the brand name and generic aspirin
We would set up the hypothesis.
The null hypothesis is
H0 : μ1 ≥ μ2 H0 : μ1 - μ2 ≥ 0
The alternative hypothesis is
H1 : μ1 < μ2 H1 : μ1 - μ2 < 0
This is a left tailed test
Since sample standard deviation is known, we would determine the test statistic by using the t test. The formula is
(x1 - x2)/√(s1²/n1 + s2²/n2)
From the information given,
x1 = 325.01
x2 = 323.47
s1 = 10.12
s2 = 11.43
n1 = 200
n2 = 180
t = (325.01 - 323.47)/√(10.12²/200 + 11.43²/180)
t = 1.24
1.237877
The formula for determining the degree of freedom is
df = [s1²/n1 + s2²/n2]²/(1/n1 - 1)(s1²/n1)² + (1/n2 - 1)(s2²/n2)²
df = [10.12²/200 + 11.43²/180]²/[(1/200 - 1)(10.12²/200)² + (1/180 - 1)(11.43²/180)²] = 1.53233946713/0.00537245359
df = 285
We would determine the probability value from the t test calculator. It becomes
p value = 0.108
Since alpha, 0.025 < than the p value, 0.108, then we would fail to reject the null hypothesis. Therefore, at 2.5% level of significance, these data support the brand name producers claim
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