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Elenna [48]
3 years ago
11

Two athletic shoe brands were studied to see which brand was manufactured with the least amount of defects. Brand A was found to

have 5 out of 600 pairs of shoes with a defect. Brand B was found to have 5 out of 250 pairs of shoes with a defect.
For Brand A, how many defects should they expect after manufacturing 2500 pairs of shoes?
Mathematics
1 answer:
avanturin [10]3 years ago
3 0

Answer:

21

Step-by-step explanation:

We need to determine the percentage of defects of shoes for brand A

Percentage of shoes that have defect = (5/600) x 100 = 0.83%

shoes that would have defects when 2500 shoes are manufactured = 0.83% x 2500 = 0.0083 x 2500 = 20.75

rounding off to the nearest whole number gives 21 shoes

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A billing company that collects bills for​ doctors' offices in the area is concerned that the percentage of bills being paid by
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1) A. H0: p = 0.30

HA: p not equal to 0.30

2) A. The Independence Assumption is met.

C. The Randomization Condition is met.

D. The Success/Failure Condition is met.

3) Test statistic z = 2.089

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4) C. Reject H0. There is sufficient evidence to suggest that the percentage of bills paid by medical insurance has changed.

Step-by-step explanation:

1) This is a hypothesis test for a proportion.

The claim is that there is a significant change in the percent of bills being paid by medical​ insurance.

As we are looking for evidence of a difference, no matter if it is higher or lower than the null hypothesis proportion, the alternative hypothesis is defined by a unequal sign.

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2) Cheking the conditions:

The independence assumption and the randomization condition are met as the bills were selected randomly from the population.

The 10% condition can not be checked, as we do not know the size of the population.

The success/failure condition is met as the products np and n(1-p) are bigger than 10 (the number of successes and failures are both bigger than 10).

3) The significance level is assumed to be 0.05.

The sample has a size n=9260.

The sample proportion is p=0.31.

 

The standard error of the proportion is:

\sigma_p=\sqrt{\dfrac{\pi(1-\pi)}{n}}=\sqrt{\dfrac{0.3*0.7}{9260}}\\\\\\ \sigma_p=\sqrt{0.000023}=0.005

Then, we can calculate the z-statistic as:

z=\dfrac{p-\pi-0.5/n}{\sigma_p}=\dfrac{0.31-0.3-0.5/9260}{0.005}=\dfrac{0.01}{0.005}=2.089

This test is a two-tailed test, so the P-value for this test is calculated as:

\text{P-value}=2\cdot P(z>2.089)=0.0367

As the P-value (0.0184) 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 there is a significant change in the percent of bills being paid by medical​ insurance.

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