Temperature is used to measure the output of a production process. When the process is in control, the mean of the process is u = 128.5 and the standard deviation is o = .4. a. Construct the X control chart and upper control limit and lower control limit for this process if samples of size 6 are used. (to 2 decimals). UCL LCL b. Determine if the process is in control for a sample with data 128.6, 128.0, 129.2, 128.8, 128.5, and 129.0. What is the value of X (to 2 decimals)? What is your conclusion? Select C. Determine if the process is in control for a sample with data 129.0, 128.8, 128.4, 129.0, 129.5, and 129.4. What is the value of x (to 2 decimals)? What is your conclusion? Select
Temperature is used to measure the output of a production process. When the process is in control, the mean of the process is u = 128.5 and the standard deviation is o = .4. a. Construct the X control chart and upper control limit and lower control limit for this process if samples of size 6 are used. (to 2 decimals). UCL LCL b. Determine if the process is in control for a sample with data 128.6, 128.0, 129.2, 128.8, 128.5, and 129.0. What is the value of X (to 2 decimals)? What is your conclusion? Select C. Determine if the process is in control for a sample with data 129.0, 128.8, 128.4, 129.0, 129.5, and 129.4. What is the value of x (to 2 decimals)? What is your conclusion? Select
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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![**Temperature Monitoring in Production Process**
Temperature is crucial for monitoring the output of a production process. When the process operates under control, the mean (μ) is 128.5 with a standard deviation (σ) of 0.4. Below are the tasks and calculations for determining the control limits and assessing process control status using sample data.
### a. Constructing Control Limits
To construct the \( \bar{X} \) control chart with an upper control limit (UCL) and lower control limit (LCL) for samples of size 6:
- **UCL**: [Input Box]
- **LCL**: [Input Box]
### b. Sample Analysis: Data Set 1
*Evaluate if the process is in control using the sample data: 128.6, 128.0, 129.2, 128.8, 128.5, 129.0.*
- **Calculate \( \bar{X} \)** (to 2 decimals): [Input Box]
- **Conclusion**: [Dropdown Menu: "In Control"/"Out of Control"]
### c. Sample Analysis: Data Set 2
*Evaluate if the process is in control using the sample data: 129.0, 128.8, 128.4, 129.0, 129.5, 129.4.*
- **Calculate \( \bar{X} \)** (to 2 decimals): [Input Box]
- **Conclusion**: [Dropdown Menu: "In Control"/"Out of Control"]
Use this guide to evaluate and ensure that the production process maintains the desired output quality.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F8c845cf8-3d77-4ddd-9d5a-2a560adfc5de%2Fbbbf01b8-f70f-4968-8496-7df1cd58abc5%2F651owl_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Temperature Monitoring in Production Process**
Temperature is crucial for monitoring the output of a production process. When the process operates under control, the mean (μ) is 128.5 with a standard deviation (σ) of 0.4. Below are the tasks and calculations for determining the control limits and assessing process control status using sample data.
### a. Constructing Control Limits
To construct the \( \bar{X} \) control chart with an upper control limit (UCL) and lower control limit (LCL) for samples of size 6:
- **UCL**: [Input Box]
- **LCL**: [Input Box]
### b. Sample Analysis: Data Set 1
*Evaluate if the process is in control using the sample data: 128.6, 128.0, 129.2, 128.8, 128.5, 129.0.*
- **Calculate \( \bar{X} \)** (to 2 decimals): [Input Box]
- **Conclusion**: [Dropdown Menu: "In Control"/"Out of Control"]
### c. Sample Analysis: Data Set 2
*Evaluate if the process is in control using the sample data: 129.0, 128.8, 128.4, 129.0, 129.5, 129.4.*
- **Calculate \( \bar{X} \)** (to 2 decimals): [Input Box]
- **Conclusion**: [Dropdown Menu: "In Control"/"Out of Control"]
Use this guide to evaluate and ensure that the production process maintains the desired output quality.
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