Respuesta :
Answer:
Yes. There is enough evidence to support the claim that the proportion of individuals who caught pertussis and were not up to date on their booster shot is higher than those that were.
P-value = 0.00013.
Step-by-step explanation:
This is a hypothesis test for the difference between proportions.
The claim is that the proportion of individuals who caught pertussis and were not up to date on their booster shot is higher than those that were.
Then, the null and alternative hypothesis are:
[tex]H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2> 0[/tex]
The significance level is 0.05.
The sample 1 (Group 1), of size n1=145 has a proportion of p1=0.124.
[tex]p_1=X_1/n_1=18/145=0.124[/tex]
The sample 2 (Group 2), of size n2=355 has a proportion of p2=0.037.
[tex]p_2=X_2/n_2=13/355=0.037[/tex]
The difference between proportions is (p1-p2)=0.088.
[tex]p_d=p_1-p_2=0.124-0.037=0.088[/tex]
The pooled proportion, needed to calculate the standard error, is:
[tex]p=\dfrac{X_1+X_2}{n_1+n_2}=\dfrac{18+13.135}{145+355}=\dfrac{31}{500}=0.062[/tex]
The estimated standard error of the difference between means is computed using the formula:
[tex]s_{p1-p2}=\sqrt{\dfrac{p(1-p)}{n_1}+\dfrac{p(1-p)}{n_2}}=\sqrt{\dfrac{0.062*0.938}{145}+\dfrac{0.062*0.938}{355}}\\\\\\s_{p1-p2}=\sqrt{0+0}=\sqrt{0.001}=0.024[/tex]
Then, we can calculate the z-statistic as:
[tex]z=\dfrac{p_d-(\pi_1-\pi_2)}{s_{p1-p2}}=\dfrac{0.088-0}{0.024}=\dfrac{0.088}{0.024}=3.68[/tex]
This test is a right-tailed test, so the P-value for this test is calculated as (using a z-table):
[tex]\text{P-value}=P(z>3.68)=0.00013[/tex]
As the P-value (0.00013) is smaller than the significance level (0.05), the effect is significant.
The null hypothesis is rejected.
There is enough evidence to support the claim that the proportion of individuals who caught pertussis and were not up to date on their booster shot is higher than those that were.
