Observing Processes Over Time (Section 1.2.5)
This session illustrates the role of visual statistical tools for understanding process variability, detecting instability, and supporting engineering decision-making in disciplines such as mechanical, electrical, and systems engineering.
1. Dot Plot: Observing Variation
Key Knowledge: Dot plots help visualize distribution but don't show temporal patterns. Mechanical and process engineers can use this to assess batch variability, such as temperature fluctuation during alloy cooling cycles.
2. Time Series Plot: Detecting Shifts Over Time
Key Knowledge: Time plots help detect non-random patterns and process drifts. Electrical engineers monitoring voltage stability can spot shifts caused by component fatigue or environmental changes.
3. Deming’s Funnel Experiment: Learning About Overcontrol
Key Knowledge: Overadjusting based on random variation increases overall error. This is crucial in robotics or CNC machining: automatic corrections without signal/noise understanding may degrade precision.
4. Control Chart: Decision Making with Statistical Limits
Key Knowledge: Control charts are tools for stability analysis. Mechanical engineers can use them to ensure pressure levels remain within specs in a hydraulic actuator, or electrical engineers for production yield quality.