Answer:
The value of the test statistic is -1.91.
Step-by-step explanation:
We are given that a publisher reports that 79% of their readers own a laptop. A random sample of 150 found that 72% of the readers owned a laptop.
A marketing executive wants to test the claim that the percentage is actually different from the reported percentage.
Let p = population % of readers who own a laptop
SO, Null Hypothesis, [tex]H_0[/tex] : p = 79% {means that the percentage is same as that of the reported percentage}
Alternate Hypothesis, [tex]H_a[/tex] : p [tex]\neq[/tex] 79% {means that the percentage is actually different from the reported percentage}
The test statistics that will be used here is One-sample z proportion statistics;
T.S. = [tex]\frac{\hat p-p}{\sqrt{\frac{\hat p(1- \hat p)}{n} } }[/tex] ~ N(0,1)
where, [tex]\hat p[/tex] = % of the readers who owned a laptop in a sample of 150 readers = 72%
n = sample of readers = 150
So, test statistics = [tex]\frac{0.72-0.79}{\sqrt{\frac{0.72(1- 0.72)}{150} } }[/tex]
= -1.91
Therefore, the value of the test statistic is -1.91.
