An SRS of 165 flights of a large airline (call this airline 1) showed that 137 were on time. An SRS of 165 flights of another large airline (call this airline 2) showed that 149 were on time. Let p, and p2 be the proportion of all flights that are on time for these two respective airlines. Is airline 2 more reliable? In other words, does airline 1 have a lower on-time rate compared to airline 2? Carry out an appropriate test using a significance level of 0.02. (a) What are the null and alternative hypotheses? Ho: P 0.0 HA: p 0.0 (b) What is the test statistic? (Round your answer to 2 decimal places, if needed.) (c) Using the statistical table, what is the p-value? (Round your answer to 4 decimal places, if needed.) (d) Based on the p-value, fail to reject + the null hypothesis.

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### Hypothesis Testing for Airline On-Time Performance

An SRS (simple random sample) of 165 flights from a large airline (referred to as airline 1) showed 137 flights were on time. A similar SRS of 165 flights from another large airline (referred to as airline 2) showed 149 flights were on time. Let \( p_1 \) and \( p_2 \) be the proportion of all flights that are on time for airlines 1 and 2, respectively. To determine if airline 2 is more reliable, we check if airline 1 has a lower on-time rate compared to airline 2. Perform the appropriate statistical test at a significance level of 0.02.

#### (a) What are the null and alternative hypotheses?

- **Null Hypothesis (\( H_0 \))**: \( p_1 < p_2 \)
- **Alternative Hypothesis (\( H_A \))**: \( p_1 = p_2 \)

#### (b) What is the test statistic?

- Fill in your computed test statistic here, rounding your answer to 2 decimal places if needed.

#### (c) Using the statistical table, what is the p-value?

- Enter the calculated p-value here, rounding your answer to 4 decimal places if needed.

#### (d) Based on the p-value, what decision should be made regarding the null hypothesis?

- Conclusion: Based on the p-value, **fail to reject** the null hypothesis. 

This educational example illustrates hypothesis testing using sample data to assess if there is a significant difference in the on-time performance between two airlines at a 0.02 significance level.
Transcribed Image Text:### Hypothesis Testing for Airline On-Time Performance An SRS (simple random sample) of 165 flights from a large airline (referred to as airline 1) showed 137 flights were on time. A similar SRS of 165 flights from another large airline (referred to as airline 2) showed 149 flights were on time. Let \( p_1 \) and \( p_2 \) be the proportion of all flights that are on time for airlines 1 and 2, respectively. To determine if airline 2 is more reliable, we check if airline 1 has a lower on-time rate compared to airline 2. Perform the appropriate statistical test at a significance level of 0.02. #### (a) What are the null and alternative hypotheses? - **Null Hypothesis (\( H_0 \))**: \( p_1 < p_2 \) - **Alternative Hypothesis (\( H_A \))**: \( p_1 = p_2 \) #### (b) What is the test statistic? - Fill in your computed test statistic here, rounding your answer to 2 decimal places if needed. #### (c) Using the statistical table, what is the p-value? - Enter the calculated p-value here, rounding your answer to 4 decimal places if needed. #### (d) Based on the p-value, what decision should be made regarding the null hypothesis? - Conclusion: Based on the p-value, **fail to reject** the null hypothesis. This educational example illustrates hypothesis testing using sample data to assess if there is a significant difference in the on-time performance between two airlines at a 0.02 significance level.
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