Control Charting

Correct Answer: The process exhibits non-random variation indicating over-control

When 15 consecutive points fall within one standard deviation of the mean on a control chart, this indicates a process that is exhibiting non-random variation in the form of over-control. This pattern suggests that the process is being micro-managed or over-adjusted, as natural statistical processes typically show more variation. Normal random variation would have points occasionally falling beyond one standard deviation. The other answers are incorrect because: - Natural statistical fluctuation would show more scattered points, not consistent clustering - Excessive variability would show points far from the mean, not clustered close to it - Optimal performance would show balanced variation within control limits, not artificially constrained variation This situation actually indicates a problem that needs investigation, as over-control can lead to increased costs and schedule inefficiencies through unnecessary interventions in the process.

Correct Answer: A saw-tooth pattern

A saw-tooth pattern in a control chart is characterized by points alternating up and down across the mean for eight or more consecutive points. This pattern suggests a shift in process performance and indicates potential system instability. This pattern is distinct from: Cyclic patterns which show regular, predictable up and down movements often related to seasonal or periodic variations - these have a more regular wavelength and amplitude. Random patterns stay within control limits but lack the consistent alternating up-down movement across the mean. Trend patterns show a consistent directional movement (either upward or downward) rather than alternating movements. The saw-tooth pattern is significant because it suggests the presence of two distinct systematic influences affecting the process in opposite directions, which requires investigation and correction.

Correct Answer: The Upper or Lower Control Limit (UCL/LCL)

In control charts, data points that fall exactly three standard deviations from the mean represent the Upper Control Limit (UCL) or Lower Control Limit (LCL). This is a fundamental principle in statistical process control. The control limits at ±3 sigma (standard deviations) are used to establish the natural boundaries of process variation. This is the mathematically correct and industry-standard placement of control limits. The other options are incorrect because: - Warning limits are typically set at ±2 sigma, not 3 sigma, and they do not include additional safety factors - A point at exactly 3 sigma is the control limit itself, not an anomaly requiring adjustment - While 3 sigma marks are related to variation types, they specifically represent the control limits rather than just being a boundary between common and special cause variations