A manufacturer turns out a product item that is labeled either “defective” or “not defective.” In order to estimate the proportion defective, a random sample of 100 items is taken from production, and 10 are found to be defective. Following implementation of a quality improvement program, the experiment is conducted again. A new sample of 100 is taken, and this time only 6 are found to be defective. (a) Give a 95% confidence interval on p_{1}-p_{2},
p1
−p2
, where p_1
p1
is the population proportion defective before improvement and p_2
p2
is the proportion defective after improvement. (b) Is there information in the confidence interval found in (a) that would suggest that p_{1}>p_{2} ?
p1
>p2
? Explain.
Let us make a summary of the information given about the samples.
Sample 1(before improvement)
"n_1=100"
"x_1=10"
sample 2(after improvement)
"n_2=100"
"x_2=6"
Point estimate for proportion in sample 1 is given as,
"\\hat{p}_1=x_1\/n_1=10\/100=0.1"
For sample 2,
"\\hat{p}_2=x_2\/n_2=6\/100=0.06"
Define "\\hat{q}_1=1-\\hat{p}_1\\space and\\space \\hat{q}_2=1-\\hat{p}_2"
a.
A 95% confidence interval for the difference in proportions"(p_1-p_2)" is given as,
"C.I=(\\hat{p}_1-\\hat{p}_2)\\pm Z_{\\alpha\/2}*\\sqrt{(\\hat{p}_1*\\hat{q}_1)\/n_1+(\\hat{p}_2*\\hat{q}_2)\/n_2}" , where "Z_{\\alpha\/2}=Z_{0.05\/2}=Z_{0.025}=1.96" is the value in the standard normal distribution that leaves an area of 0.025 to the right.
Now,
"C.I=(0.1-0.06)\\pm 1.96*\\sqrt{(0.09\/100)+(0.0564\/100)}"
"C.I=0.04\\pm 1.96*\\sqrt{0.001464}"
"C.I=0.04\\pm 1.96*0.03826225"
"C.I=0.04\\pm0.07499401"
Therefore, a 95% confidence interval for "(p_1-p_2)" is given as, "(-0.035,\\space 0.115)"
b.
No.
If "p_1\\gt p_2" then we would expect that this interval would not include 0. Since this confidence interval includes zero, there is no sufficient evidence to show that "p_1\\gt p_2"
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