Here, we have sample proportion, "p=50\/600=0.083" and the sample size, "n=600"
Suppose the null hypotheses, "H_0: P=0.10"
Alternate hypotheses, "H_1:P<0.10"
First we will calculate the standard deviation of the sampling distribution,
"\\sigma=\\sqrt{P*(1-P)\/n}"
where P is the hypothesized value of population proportion in the null hypothesis, and n is the sample size.
"\\sigma=\\sqrt{(0.10*0.90)\/600}"
"=\\sqrt{0.09\/600}"
"=0.0122"
Since the data are simple random sample from the population of interest, we can use Z-test.
"z=(p-P)\/\\sigma"
"=(0.083-0.10)\/0.0122"
"=-0.017\/0.0122"
"=-1.39"
Since we have a one tailed test, the P-value is the probability that the z-score is less than -1.39.
We will use normal distribution table to find "P(z<-1.39)=0.082" .
Thus, P-value = 0.082
Since the p-value is greater than the significance level, "\\alpha=0.05" , we cannot reject the null hypothesis.
So, there is not strong evidence to say that less than 10 % of all high school boys who take diet pills will lose weight.
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