The accompanying table provides the data for 100 room inspections at each of 25 hotels in a major chain. Management would like the proportion of nonconforming rooms to be less than 2%. Formulate a one-sample hypothesis test for a proportion and perform the calculations using the correct formulas and Excel functions. Use a level of significance of 0.05. E Click the icon to view the room inspection data. Is there sufficient evidence at the 0.05 level of significance that the proportion of nonconforming rooms to be less than 2%? Determine the null hypothesis, H,, and the alternative hypothesis, H,- H, (Type integers or decimals. Do not round.) Compute the test statistic. (Round to two decimal places as needed.) Find the p-value for the test. |(Round to three decimal places as needed.) State the conclusion. The p-value is the chosen value of a, so V the null hypothesis. There is evidence to conclude that the proportion of nonconforming rooms will be less than 2%.

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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The image presents a statistical analysis scenario for a major hotel chain, where the goal is to ensure that the proportion of nonconforming rooms is less than 2%. The user is required to conduct a one-sample hypothesis test for a proportion, using a significance level of 0.05.

**Step-by-Step Instructions:**

1. **State the Hypotheses:**
   - **Null Hypothesis (H₀):** The proportion of nonconforming rooms is equal to or greater than 2% (p ≥ 0.02).
   - **Alternative Hypothesis (H₁):** The proportion of nonconforming rooms is less than 2% (p < 0.02).

2. **Data Analysis:**
   - Inspect the room data from 25 samples, each comprising 100 room inspections. The dataset includes the number of nonconforming rooms and the fraction of nonconforming rooms for each sample.

3. **Room Inspection Data:**
   - Samples 1 to 25 have their rooms inspected, and nonconforming results recorded as follows:
     - For instance, Sample 1 shows 100 rooms inspected, with 3 nonconforming rooms (a fraction of 0.03).
     - Sample 8 shows 100 rooms inspected, with 7 nonconforming rooms (a fraction of 0.07).
     - Several samples, like Sample 3 and Sample 24, show no nonconforming rooms (a fraction of 0.00).

4. **Perform Calculations:**
   - Compute the test statistic based on the data provided.
   - Round the answer to two decimal places.

5. **Determine the p-value:**
   - Find and round the p-value to three decimal places to assess the evidence against the null hypothesis.

6. **Draw a Conclusion:**
   - Compare the p-value with the chosen significance level (α = 0.05).
   - Decide whether to reject or fail to reject the null hypothesis.
   - State if there is sufficient evidence to conclude that the proportion of nonconforming rooms is less than 2%.

This analysis is essential for ensuring quality control within the hotel chain and maintaining acceptable standards of service.
Transcribed Image Text:The image presents a statistical analysis scenario for a major hotel chain, where the goal is to ensure that the proportion of nonconforming rooms is less than 2%. The user is required to conduct a one-sample hypothesis test for a proportion, using a significance level of 0.05. **Step-by-Step Instructions:** 1. **State the Hypotheses:** - **Null Hypothesis (H₀):** The proportion of nonconforming rooms is equal to or greater than 2% (p ≥ 0.02). - **Alternative Hypothesis (H₁):** The proportion of nonconforming rooms is less than 2% (p < 0.02). 2. **Data Analysis:** - Inspect the room data from 25 samples, each comprising 100 room inspections. The dataset includes the number of nonconforming rooms and the fraction of nonconforming rooms for each sample. 3. **Room Inspection Data:** - Samples 1 to 25 have their rooms inspected, and nonconforming results recorded as follows: - For instance, Sample 1 shows 100 rooms inspected, with 3 nonconforming rooms (a fraction of 0.03). - Sample 8 shows 100 rooms inspected, with 7 nonconforming rooms (a fraction of 0.07). - Several samples, like Sample 3 and Sample 24, show no nonconforming rooms (a fraction of 0.00). 4. **Perform Calculations:** - Compute the test statistic based on the data provided. - Round the answer to two decimal places. 5. **Determine the p-value:** - Find and round the p-value to three decimal places to assess the evidence against the null hypothesis. 6. **Draw a Conclusion:** - Compare the p-value with the chosen significance level (α = 0.05). - Decide whether to reject or fail to reject the null hypothesis. - State if there is sufficient evidence to conclude that the proportion of nonconforming rooms is less than 2%. This analysis is essential for ensuring quality control within the hotel chain and maintaining acceptable standards of service.
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