Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
1. Intro to Stats and Collecting Data
Intro to Stats
Problem 14.CQQ.5
Textbook Question
In Exercises 5–8, use the following two control charts that result from testing batches of newly manufactured aircraft altimeters, with 100 in each batch. The original sample values are errors (in feet) obtained when the altimeters are tested in a pressure chamber that simulates an altitude of 6000 ft. The Federal Aviation Administration requires an error of no more than 40 ft at that altitude.
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Is the process variation within statistical control? Why or why not?

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Step 1: Understand the concept of process variation and statistical control. Process variation refers to the natural or inherent variability in a manufacturing process. A process is considered to be within statistical control if the variation is due only to common causes (natural variation) and not special causes (unexpected or assignable variation). Control charts are used to monitor this.
Step 2: Examine the control charts provided in the problem. Typically, control charts include an upper control limit (UCL), a lower control limit (LCL), and a centerline (CL). These limits are calculated based on the data and represent the boundaries within which the process is expected to operate under normal conditions.
Step 3: Check if any data points fall outside the control limits. If any points are outside the UCL or LCL, this indicates the presence of special causes of variation, and the process is not within statistical control.
Step 4: Look for patterns or trends in the data points within the control limits. Even if all points are within the limits, certain patterns (e.g., a run of consecutive points above or below the centerline, or a systematic trend) can indicate that the process is not in control.
Step 5: Based on the observations from the control charts, determine whether the process variation is within statistical control. If all points are within the control limits and there are no unusual patterns, the process is likely within statistical control. Otherwise, it is not, and further investigation is needed to identify and address the special causes of variation.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Control Charts
Control charts are graphical tools used to monitor the stability of a process over time. They display data points in time order and include control limits that indicate the expected variation in the process. If data points fall outside these limits, it suggests that the process may be out of control, prompting further investigation into potential causes of variation.
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Process Variation
Process variation refers to the natural fluctuations that occur in a manufacturing or testing process. It can be categorized into two types: common cause variation, which is inherent to the process, and special cause variation, which arises from specific, identifiable factors. Understanding the type of variation is crucial for determining whether a process is stable and predictable.
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Statistical Control
A process is said to be in statistical control when its variation is consistent and predictable, typically indicated by data points falling within the control limits on a control chart. If the process shows patterns or points outside these limits, it suggests that it is not in control, which may require corrective actions to address underlying issues affecting quality.
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