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Compute the test statistic. Find the​ P-value of the test statistic. Does the use of the single line appear to red

Compute the test statistic. Find the​ P-value of the test statistic. Does the use of the single line appear to red

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Compute the test statistic.

Find the​ P-value of the test statistic.

Does the use of the single line appear to reduce the variation among waiting​ times?

### Hypothesis Testing on Bank Waiting Times The given scenario involves analyzing customer waiting times at a bank with a single waiting line for three teller windows. The goal is to test whether the standard deviation of waiting times is less than 2.3 minutes, which is the standard deviation for separate waiting lines for each teller. A significance level of 0.025 is used for the analysis. #### Steps to Complete the Analysis a. **Identify the Null and Alternative Hypotheses** - **Null Hypothesis (H₀):** σ = 2.3 minutes - **Alternative Hypothesis (Hₐ):** σ < 2.3 minutes The correct choice is marked as option A on the diagram. b. **Compute the Test Statistic** - The diagram includes a space for calculating the chi-square (χ²) test statistic, meant to be rounded to two decimal places as needed. This process requires analyzing the data, which can be accessed through a clickable icon, followed by statistical calculations to determine whether the null hypothesis can be rejected in favor of the alternative hypothesis. *Note: Further computation details are provided as part of the test statistic analysis.*
Transcribed Image Text:### Hypothesis Testing on Bank Waiting Times The given scenario involves analyzing customer waiting times at a bank with a single waiting line for three teller windows. The goal is to test whether the standard deviation of waiting times is less than 2.3 minutes, which is the standard deviation for separate waiting lines for each teller. A significance level of 0.025 is used for the analysis. #### Steps to Complete the Analysis a. **Identify the Null and Alternative Hypotheses** - **Null Hypothesis (H₀):** σ = 2.3 minutes - **Alternative Hypothesis (Hₐ):** σ < 2.3 minutes The correct choice is marked as option A on the diagram. b. **Compute the Test Statistic** - The diagram includes a space for calculating the chi-square (χ²) test statistic, meant to be rounded to two decimal places as needed. This process requires analyzing the data, which can be accessed through a clickable icon, followed by statistical calculations to determine whether the null hypothesis can be rejected in favor of the alternative hypothesis. *Note: Further computation details are provided as part of the test statistic analysis.*
**Customer Waiting Times (in minutes)** This table displays the waiting times in minutes for customers. It consists of multiple columns with the recorded times: | 7.8 | 7.5 | 5.5 | 7.1 | |-----|-----|-----|-----| | 6.5 | 7.3 | 6.5 | 6.2 | | 6.9 | 7.1 | 8.3 | 6.2 | | 7.8 | 7.8 | 5.6 | 7.8 | | 6.6 | 6.6 | 7.5 | 7.7 | | 6.3 | 7.8 | 7.6 | 11.9 | | 7.1 | 7.8 | 6.9 | 7.6 | | 6.8 | 7.8 | 8.2 | 7.4 | | 6.8 | 7.3 | 6.5 | 6.4 | | 7.3 | 6.9 | 6.6 | 7.2 | | 8.8 | 6.5 | 11.8| 6.7 | | 8.8 | 6.8 | 6.9 | 6.4 | | 6.3 | 7.6 | 7.6 | 6.2 | | 7.9 | 6.1 | 6.2 | 6.3 | | 6.7 | 8.1 | 7.5 | 7.5 | This data can be useful for analyzing trends in wait times, assessing efficiency, and identifying areas for improvement in service delivery.
Transcribed Image Text:**Customer Waiting Times (in minutes)** This table displays the waiting times in minutes for customers. It consists of multiple columns with the recorded times: | 7.8 | 7.5 | 5.5 | 7.1 | |-----|-----|-----|-----| | 6.5 | 7.3 | 6.5 | 6.2 | | 6.9 | 7.1 | 8.3 | 6.2 | | 7.8 | 7.8 | 5.6 | 7.8 | | 6.6 | 6.6 | 7.5 | 7.7 | | 6.3 | 7.8 | 7.6 | 11.9 | | 7.1 | 7.8 | 6.9 | 7.6 | | 6.8 | 7.8 | 8.2 | 7.4 | | 6.8 | 7.3 | 6.5 | 6.4 | | 7.3 | 6.9 | 6.6 | 7.2 | | 8.8 | 6.5 | 11.8| 6.7 | | 8.8 | 6.8 | 6.9 | 6.4 | | 6.3 | 7.6 | 7.6 | 6.2 | | 7.9 | 6.1 | 6.2 | 6.3 | | 6.7 | 8.1 | 7.5 | 7.5 | This data can be useful for analyzing trends in wait times, assessing efficiency, and identifying areas for improvement in service delivery.
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