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Using Confidence Intervals

a. Assume that we want to use a 0.05 significance level to test the claim that p1 < p2. Which is better: A hypothesis test or a confidence interval?

b. In general, when dealing with inferences for two population proportions, which two of the following are equivalent: confidence interval method; P-value method; critical value method?

c. If we want to use a 0.05 significance level to test the claim that p1 < p2, what confidence level should we use?

d. If we test the claim in part (c) using the sample data in Exercise 1, we get this confidence interval: -0.000508 < p1 - p2 < - 0.000309. What does this confidence interval suggest about the claim?

Short Answer

Expert verified

a. A hypothesis test is better than a confidence interval.

b. The p-value method and critical value method are equivalent.

c. The 90% confidence interval to test the claim is made about the difference between two population proportion \({p_1}{\rm{ and }}{p_2}\).

d. The given confidence interval suggests that there is sufficient evidence to support the claim that \({p_1} < {p_2}\).

Step by step solution

01

Describe the methods of Inferences about two proportion.

a.The following methods can be used for comparing two population proportion:

1. Hypothesis test

2. Confidence Interval

The hypothesis test is used to test the claims about two population proportions. The confidence interval is used when estimating about the differences between two population proportions.

A hypothesis test is recommended for testing a claim at 0.05 level of significance.

Hence, in this case hypothesis test is better than confidence interval.

02

Check equivalency of three methods

b.In a hypothesis test, there are two methods to test the claims about population proportion,

1. P-value method

2. Critical value method

Both methods are used to test the claim made about two proportions. In p-value method, a probability value is compared to a significance level to make a decision while in critical value method, the test statistic is compared to critical value(s) to make a decision about hypotheses.

The method of confidence interval is used primarily to estimate the difference between two population proportions.

Thus, the P-value method and critical value method are equivalent.

03

State the confidence level

c. As per the table 8-1, for one tailed significance test at 0.05 level of significance, 90% confidence level is recommended.

04

Step 4:Make conclusion about the claim

d. Refer to exercise 1 for the claim stated as,

\(\begin{array}{l}{H_o}:{p_1} = {p_2}\\{H_a}:{p_1} < {p_2}\end{array}\)

Where \({p_1},{p_2}\) are population proportion of children who developed polio after vaccine and in control group respectively.

The given confidence interval is\( - 0.000508 < {p_1} - {p_2} < - 0.000309\).

This confidence does not contain zero. Thus, there is significant difference between two proportion\({{\rm{p}}_{\rm{1}}}{\rm{ and }}{{\rm{p}}_{\rm{2}}}\).

Therefore, the given confidence interval suggests that there is sufficient evidence to support the claim that \({p_1}{\rm{ }}\)is less that \({p_2}\).

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Most popular questions from this chapter

Testing Claims About Proportions. In Exercises 7鈥22, test the given claim. Identify the null hypothesis, alternative hypothesis, test statistic, P-value or critical value(s), then state the conclusion about the null hypothesis, as well as the final conclusion that addresses the original claim.

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Determining Sample Size The sample size needed to estimate the difference between two population proportions to within a margin of error E with a confidence level of 1 - a can be found by using the following expression:

\({\bf{E = }}{{\bf{z}}_{\frac{{\bf{\alpha }}}{{\bf{2}}}}}\sqrt {\frac{{{{\bf{p}}_{\bf{1}}}{{\bf{q}}_{\bf{1}}}}}{{{{\bf{n}}_{\bf{1}}}}}{\bf{ + }}\frac{{{{\bf{p}}_{\bf{2}}}{{\bf{q}}_{\bf{2}}}}}{{{{\bf{n}}_{\bf{2}}}}}} \)

Replace \({{\bf{n}}_{\bf{1}}}\;{\bf{and}}\;{{\bf{n}}_{\bf{2}}}\) by n in the preceding formula (assuming that both samples have the same size) and replace each of \({{\bf{p}}_{\bf{1}}}{\bf{,}}{{\bf{q}}_{\bf{1}}}{\bf{,}}{{\bf{p}}_{\bf{2}}}\;{\bf{and}}\;{{\bf{q}}_{\bf{2}}}\)by 0.5 (because their values are not known). Solving for n results in this expression:

\({\bf{n = }}\frac{{{\bf{z}}_{\frac{{\bf{\alpha }}}{{\bf{2}}}}^{\bf{2}}}}{{{\bf{2}}{{\bf{E}}^{\bf{2}}}}}\)

Use this expression to find the size of each sample if you want to estimate the difference between the proportions of men and women who own smartphones. Assume that you want 95% confidence that your error is no more than 0.03.

Braking Reaction Times: Histogram Listed below are sorted braking reaction times (in 1>10,000 sec) for male and female subjects (based on data from the RT-2S Brake Reaction Time Tester). Construct a histogram for the reaction times of males. Use a class width of 8 and use 28 as the lower limit of the first class. For the horizontal axis, use class midpoint values. Does it appear that the data are from a population with a normal distribution?

Males

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Repeat Exercise 12 鈥淚Q and Lead鈥 by assuming that the two population standard deviations are equal, so \({\sigma _1} = {\sigma _2}\). Use the appropriate method from Part 2 of this section. Does pooling the standard deviations yield results showing greater significance?

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