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Sagot :
From the test the person wants, and the sample data, we build the test hypothesis, find the test statistic, and use this to reach a conclusion.
This is a two-sample test, thus, it is needed to understand the central limit theorem and subtraction of normal variables.
Doing this:
- The null hypothesis is [tex]H_0: p_1 - p_2 = 0 \rightarrow p_1 = p_2[/tex]
- The alternative hypothesis is [tex]H_1: p_1 - p_2 \neq 0 \rightarrow p_1 \neq p_2[/tex]
- The value of the test statistic is z = -2.67.
- The p-value of the test is 0.0076 < 0.05(standard significance level), which means that there is enough evidence to conclude that the proportion of people who wear life vests while riding a jet ski is not the same as the proportion of people who wear life vests while riding in a boat.
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Central Limit Theorem
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
Subtraction between normal variables:
When two normal variables are subtracted, the mean is the difference of the means, while the standard deviation is the square root of the sum of the variances.
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Proportion 1: Jet-ski users
86.5% out of 400, thus:
[tex]p_1 = 0.865[/tex]
[tex]s_1 = \sqrt{\frac{0.865*0.135}{400}} = 0.0171[/tex]
Proportion 2: boaters
92.8% out of 250, so:
[tex]p_2 = 0.928[/tex]
[tex]s_2 = \sqrt{\frac{0.928*0.072}{250}} = 0.0163[/tex]
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Hypothesis:
Test the claim that the proportion of people who wear life vests while riding a jet ski is not the same as the proportion of people who wear life vests while riding in a boat.
At the null hypothesis, it is tested that the proportions are the same, that is, the subtraction is 0. So
[tex]H_0: p_1 - p_2 = 0 \rightarrow p_1 = p_2[/tex]
At the alternative hypothesis, it is tested that the proportions are different, that is, the subtraction is different of 0. So
[tex]H_1: p_1 - p_2 \neq 0 \rightarrow p_1 \neq p_2[/tex]
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Test statistic:
The test statistic is:
[tex]z = \frac{X - \mu}{s}[/tex]
In which X is the sample mean, [tex]\mu[/tex] is the value tested at the null hypothesis, and s is the standard error.
0 is tested at the null hypothesis.
This means that [tex]\mu = 0[/tex]
From the samples:
[tex]X = p_1 - p_2 = 0.865 - 0.928 = -0.063[/tex]
[tex]s = \sqrt{s_1^2 + s_2^2} = \sqrt{0.0171^2 + 0.0163^2} = 0.0236[/tex]
The value of the test statistic is:
[tex]z = \frac{X - \mu}{s}[/tex]
[tex]z = \frac{-0.063 - 0}{0.0236}[/tex]
[tex]z = -2.67[/tex]
The value of the test statistic is z = -2.67.
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p-value of the test and decision:
The p-value of the test is the probability that the proportion differs by at at least 0.063, which is P(|z| > 2.67), given by 2 multiplied by the p-value of z = -2.67.
Looking at the z-table, z = -2.67 has a p-value of 0.0038.
2*0.0038 = 0.0076.
The p-value of the test is 0.0076 < 0.05(standard significance level), which means that there is enough evidence to conclude that the proportion of people who wear life vests while riding a jet ski is not the same as the proportion of people who wear life vests while riding in a boat.
A similar question is found at https://brainly.com/question/24250158
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