Red points indicate subgroups that fail at least one of the tests for special causes and are not in control. The control limits on the S chart, which are set at a distance of 3 standard deviations above and below the center line, show the amount of variation that is expected in the subgroup standard deviations. The center line is the average of all subgroup standard deviations. The S chart plots the subgroup standard deviations. If the S chart is not in control, then the control limits on the Xbar chart are not accurate. The two tests for nonrandom behavior detect trends, oscillation, mixtures, and clustering in your data.Before you interpret the Xbar chart, examine the S chart to determine whether the process variation is in control. When the subgroup size is greater than one, Run Chart also plots the subgroup means or medians and connects them with a line. Run Chart plots all of the individual observations versus the subgroup number, and draws a horizontal reference line at the median. Run Chart Stat > Quality Tools > Run Chart Use Run Chart to look for evidence of patterns in your process data, and perform two tests for non-random behavior. For details, see Using the Tests for Randomness. Run Chart performs two tests for randomness that provide information on the non-random variation due to trends, oscillation, mixtures, and clustering. A process is in control when only common causes affect the process output. The run chart shows if special causes are influencing your process. Another type of variation, called special causes, comes from outside the system and causes recognizable patterns, shifts, or trends in the data. Common cause variation is a natural part of the process. Run Chart Run Chart Overview Variation occurs in all processes. Optimization & Variation Reduction in Quality. Statistical Methods for Quality Improvement. "Randomness, Tests of," Encyclopedia of Statistical Sciences, 7, 555−562. Choose an example below: Run Chart Pareto Chart Cause-and-Effect Individual Distribution Identification Johnson Transformation Capability Analysis Capability Sixpack Gage Run Chart Gage Linearity and Bias Study Gage R&R Study (Crossed) (ANOVA method) Gage R&R Study (Crossed) (X-bar and R method) Gage R&R Study (Nested) Attribute Gage Study (Analytic Method) Attribute Agreement Analysis Multi-Vari Chart Symmetry Plot Quality Tools Stat > Quality Tools Choose one of the following: Run Chart Pareto Chart Cause-and-Effect Individual Distribution Identification Johnson Transformation Capability Analysis Normal Between/Within Nonnormal Multiple Variables (Normal) Multiple Variables (Nonnormal) Binomial Poisson Capability Sixpack Normal Nonnormal Between/Within Gage Study Gage Run Chart Gage Linearity and Bias Study Gage R&R Study (Crossed) Gage R&R Study (Nested) Attribute Gage Study (Analytic Method) Attribute Agreement Analysis Multi-Vari Chart Symmetry PlotĮxamples of Quality Tools The following examples illustrate how to use the various quality tools. Symmetry plots can help you assess whether your data come from a symmetric distribution. Multi-vari charts present analysis of variance data in graphical form to give you a "look" at your data. Cause-and-effect (fishbone) diagrams can help you organize brainstorming information about the potential causes of a problem. Pareto charts help you identify which of your problems are most significant, so you can focus improvement efforts on areas where the largest gains can be made. Quality Planning Tools Overview Quality Planning Tool Minitab offers several graphical tools to help you explore and detect quality problems and improve your process: Run charts detect patterns in your process data, and perform two tests for non-random behavior. 269 Capability Analysis for Multiple Variables - Nonnormal. 264 Capability Analysis for Multiple Variables - Normal. 253 Capability Analysis - Between/Within. 239 Individual Distribution Identification. 229 Attribute Gage Study (Analytic Method). Table Of Contents Quality Planning Tools.
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