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1 year ago

Suppose the p-value is 0.1038. what is the correct interpretation of this p-value in context?

1 answer:

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The p value 0.1038 represent the proportion of students who use drugs at this college is equal to 0.157, the probability of getting a statistic as extreme or more extreme as that observed is 0.1038.

According to the statement

we have given that the P value and we have to find the correct interpretation of this p-value in context.

So, For this purpose, we know that the

The P - Value is the In null-hypothesis significance testing, the p-value is the probability of obtaining test results at least as extreme as the result actually observed.

And in the given statement ,

The given p value is 0.1038.

And this p value is a If the proportion of students who use drugs at this college is equal to 0.157, the probability of getting a statistic as extreme or more extreme as that observed is 0.1038.

So, The p value 0.1038 represent the proportion of students who use drugs at this college is equal to 0.157, the probability of getting a statistic as extreme or more extreme as that observed is 0.1038.

Learn more about P value here

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Question:

Suppose the p-value is 0.1038. What is the correct interpretation of this p-value in context?

a.) If the proportion of students who use drugs at this college is greater than 0.157, the probability of getting a statistic as extreme or more extreme as that observed is 0.1038.

b.) There is a 0.1038 probability that the proportion of students who use drugs at this college is greater than 0.157.

c.) If the proportion of students who use drugs at this college is equal to 0.157, the probability of getting a statistic as extreme or more extreme as that observed is 0.1038.

d.) There is a 0.1038 probability that the proportion of students who use drugs at this college is equal to 0.157.

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) The National Highway Traffic Safety Administration collects data on seat-belt use and publishes results in the document Occupa

asambeis [7]

Answer:

We conclude that there is a difference in seat belt use.

Step-by-step explanation:

We are given that of 1,000 drivers 16-24 years old, 79% said they buckle up, whereas 924 of 1,100 drivers 25-69 years old said they did.

Let $p_1$ = population proportion of drivers 16-24 years old who buckle up .

$p_2$ = population proportion of drivers 25-69 years old who buckle up .

So, Null Hypothesis, $H_0$ : $p_1-p_2$ = 0 {means that there is no significant difference in seat belt use}

Alternate Hypothesis, $H_A$ : $p_1-p_2\neq$ 0 {means that there is a difference in seat belt use}

The test statistics that would be used here Two-sample z proportion statistics;

T.S. = $\frac{(\hat p_1-\hat p_2)-(p_1-p_2)}{\sqrt{\frac{\hat p_1(1-\hat p_1)}{n_1}+\frac{\hat p_2(1-\hat p_2)}{n_2} } }$ ~ N(0,1)

where, $\hat p_1$ = sample proportion of drivers 16-24 years old who buckle up = 79%

$\hat p_2$ = sample proportion of drivers 25-69 years old who buckle up = $\frac{924}{1100}$ = 84%

$n_1$ = sample of 16-24 years old drivers = 1000

$n_2$ = sample of 25-69 years old drivers = 1100

So, test statistics = $\frac{(0.79-0.84)-(0)}{\sqrt{\frac{0.79(1-0.79)}{1000}+\frac{0.84(1-0.84)}{1100} } }$

= -2.946

The value of z test statistics is -2.946.

Since, in the question we are not given with the level of significance so we assume it to be 5%. Now, at 0.05 significance level the z table gives critical values of -1.96 and 1.96 for two-tailed test.

Since our test statistics doesn't lie within the range of critical values of z, so we have sufficient evidence to reject our null hypothesis as it will fall in the rejection region due to which we reject our null hypothesis.

Therefore, we conclude that there is a difference in seat belt use.

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2 years ago

Choose the function whose graph is given by:

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Answer:

Step-by-step explanation:

<h2>Mark as brain liest and fo lllow</h2>

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2 years ago

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How do you do this, can someone help please?