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.7 .6 .5 .4 .3 .2 .1 0 -1- 012 3 Process A Process B Process C 4 5 6 7 8 9 10

.7 .6 .5 .4 .3 .2 .1 0 -1- 012 3 Process A Process B Process C 4 5 6 7 8 9 10

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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Supppose that A, B, and C all have the exact same upper and lower specification limits.  Select the correct answer from the choices below:


a. There is no difference between A’s Cp and B’s Cp
b. None of the three processes Cp values are the same.
c. There is no difference between B’s Cp and C’s Cp.
d. Processes A and C have identical Cp values.
### Gaussian Distribution Graphs for Three Processes #### Description The graph presented above illustrates the Gaussian (normal) distributions for three distinct processes: Process A, Process B, and Process C. Each of these processes is represented by a different line style on the graph. #### Line Styles for Each Process: - **Process A** is represented by a dashed line (-------). - **Process B** is represented by a solid line (──────). - **Process C** is represented by a dash-dot line (-.-.-.-.-.-). #### Graph Details: - **X-Axis (Horizontal Axis):** The x-axis ranges from 0 to 10. It represents the variable against which the probability density is plotted. - **Y-Axis (Vertical Axis):** The y-axis ranges from 0 to 0.7. It indicates the probability density function values for the respective processes. #### Analysis of the Distributions: 1. **Process A** (dashed line) has a normal distribution curve centered around 3 with a moderate spread. 2. **Process B** (solid line) exhibits a normal distribution that is sharply peaked around the value of 5, indicating a lower variance compared to Process A and Process C. 3. **Process C** (dash-dot line) has a normal distribution centered around 6 with a wider spread compared to both Process A and Process B, indicating higher variability. The graph provides a comparative visualization of how these three processes differ in terms of their central tendencies and variances. Understanding these differences can be critical in various fields such as quality control, statistical process control, and risk management. By examining these distributions, one can draw conclusions about which process exhibits more consistency (lower variance) and which one demonstrates higher variability. This information can then be used to make decisions about process improvements or to compare the stability of different systems.
Transcribed Image Text:### Gaussian Distribution Graphs for Three Processes #### Description The graph presented above illustrates the Gaussian (normal) distributions for three distinct processes: Process A, Process B, and Process C. Each of these processes is represented by a different line style on the graph. #### Line Styles for Each Process: - **Process A** is represented by a dashed line (-------). - **Process B** is represented by a solid line (──────). - **Process C** is represented by a dash-dot line (-.-.-.-.-.-). #### Graph Details: - **X-Axis (Horizontal Axis):** The x-axis ranges from 0 to 10. It represents the variable against which the probability density is plotted. - **Y-Axis (Vertical Axis):** The y-axis ranges from 0 to 0.7. It indicates the probability density function values for the respective processes. #### Analysis of the Distributions: 1. **Process A** (dashed line) has a normal distribution curve centered around 3 with a moderate spread. 2. **Process B** (solid line) exhibits a normal distribution that is sharply peaked around the value of 5, indicating a lower variance compared to Process A and Process C. 3. **Process C** (dash-dot line) has a normal distribution centered around 6 with a wider spread compared to both Process A and Process B, indicating higher variability. The graph provides a comparative visualization of how these three processes differ in terms of their central tendencies and variances. Understanding these differences can be critical in various fields such as quality control, statistical process control, and risk management. By examining these distributions, one can draw conclusions about which process exhibits more consistency (lower variance) and which one demonstrates higher variability. This information can then be used to make decisions about process improvements or to compare the stability of different systems.
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