Smoking Cessation Programs Among 198 smokers who underwent a “sustained care” program, 51 were no longer smoking after six months. Among 199 smokers who underwent a “standard care” program, 30 were no longer smoking after six months (based on data from “Sustained Care Intervention and Postdischarge Smoking Cessation Among Hospitalized Adults,” by Rigotti et al., Journal of the American Medical Association, Vol. 312, No. 7). We want to use a 0.01 significance level to test the claim that the rate of success for smoking cessation is greater with the sustained care program. Test the claim using a hypothesis test.
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 26m
- 11. Correlation1h 6m
- 12. Regression1h 35m
- 13. Chi-Square Tests & Goodness of Fit1h 57m
- 14. ANOVA1h 0m
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 9.QQ.2
Textbook Question
Test Values p_cap1, p_cap2. Find the values of and the pooled proportion p_bar obtained when testing the claim given in Exercise 1.

1
Identify the sample proportions \( \hat{p}_1 \) and \( \hat{p}_2 \) from the problem statement or data provided. These represent the proportions of successes in the two samples.
Determine the sample sizes \( n_1 \) and \( n_2 \) for the two groups. These are the total number of observations in each sample.
Calculate the pooled proportion \( \bar{p} \) using the formula: \( \bar{p} = \frac{x_1 + x_2}{n_1 + n_2} \), where \( x_1 = \hat{p}_1 \cdot n_1 \) and \( x_2 = \hat{p}_2 \cdot n_2 \).
Substitute the values of \( x_1 \), \( x_2 \), \( n_1 \), and \( n_2 \) into the formula to compute \( \bar{p} \).
Use the pooled proportion \( \bar{p} \) in further hypothesis testing or calculations as required by the problem.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Pooled Proportion
The pooled proportion, denoted as p_bar, is a weighted average of two sample proportions used in hypothesis testing. It combines the successes and failures from both samples to provide a single estimate of the proportion under the null hypothesis. This is particularly useful when comparing two proportions to determine if there is a significant difference between them.
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Difference in Proportions: Confidence Intervals
Sample Proportion
The sample proportion, represented as p_cap, is the ratio of the number of successes to the total number of observations in a sample. It serves as an estimate of the true population proportion and is calculated by dividing the count of successes by the sample size. Understanding sample proportions is essential for conducting tests of significance and making inferences about population parameters.
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Hypothesis Testing
Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), then using sample data to determine whether to reject H0. This process often includes calculating test statistics and p-values to assess the strength of evidence against the null hypothesis.
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