Finally, as mentioned, the diagnostic value of run charts is independent of the number of data points, which is not the case with control charts unless one adjusts the control limits in accordance with the number of data points. In practice I always do the run chart analysis first. I do not use any other sensitising control chart rules. There is one exception to this practice: When dealing with rare events data, it often pays to do the G or T control chart up front, as it may otherwise take very long time to detect improvement using run chart rules alone.
In this vignette I have demonstrated the use of the qicharts package to create control charts for measure and count data. Together with my vignettes on run charts it forms a reference on the typical day-to-day use of the package. It was not my intention to go deep into the theoretical basis of run and control charts.
Statistical Process Control
For that, seek out the references listed below. There are many more arguments available for the qic function than I have demonstrated here. Please study the documentation? Douglas C.
Montgomery Lloyd P. Provost, Sandra K.
Murray David B. Laney Improved control charts for attributes. Quality Engineering, 14 4 , Jacob Anhoej Jacob Anhoej, Anne Vingaard Olesen Introduction Control chart basics Types of control charts C chart for count of defects U chart for rate of defects P chart for proportion of defective units G chart for units produced between defective units I and MR charts for individual measurements Xbar and S charts for average measurements T chart for time between events Standardised control charts Prime control charts Control charts or run charts?
Statistical Quality Control: A Modern Introduction, 6ed
Introduction The purpose of this vignette is to demonstrate the use of qicharts for creating control charts. Load the qicharts package library qicharts. Lock random number generator to reproduce the charts from this vignette set.
Figure 1: I chart showing common cause variation. Figure 2: I chart, special cause variation. The formulas for calculation of control limits can be found in Montgomery and Provost C chart for count of defects To demonstrate the use of C, U and P charts for count data we will create a data frame mimicking the weekly number of hospital acquired pressure ulcers at a hospital that, on average, has patients with an average length of stay of four days.
Setup parameters m. Date '' , length. Figure 3: C chart displaying the number of defects. U chart for rate of defects The U chart is different from the C chart in that it accounts for variation in the area of opportunity, e. Figure 4: U chart displaying the rate of defects. P chart for proportion of defective units The P chart is probably the most common control chart in healthcare. Figure 5: P chart displaying the percent of defectives. G chart for units produced between defective units When defects or defectives are rare and the subgroups are small, C, U, and P charts become useless as most subgroups will have no defects.
Figure 6: G chart displaying the number of units produced between defectives. I and MR charts for individual measurements In healthcare, which, you may have guessed, is my domain, most quality data are count data.
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Figure 7 is an I chart of birth weights from 24 babies. Figure 7: I chart for individual measurements. Figure 8: Moving range chart. Xbar and S charts for average measurements If there is more than one measurement in each subgroup the Xbar and S charts will display the average and the within subgroup standard deviation respectively.
Figure 9: Xbar chart of average measurements. Figure S chart of within subgroup standard deviations. The normal curve is described as well as its probability and relevance to the philosophy of never-ending improvement.
Measurements over time
Prerequisites : This lesson is designed for participants familiar with the basics of statistical process control. A knowledge of basic mathematical skills is recommended. Description : This lesson introduces control charts and shows how to plot specific values on the control chart. The lesson also demonstrates how to determine and plot the mean, median, and range on a control chart. Prerequisites : This lesson is designed for participants familiar with the principles of statistical process control, the basic components of control charts, and the characteristics of a normal curve.
Description : This lesson explains how to interpret variable control charts in order to determine whether or not a process is in statistical control. The lesson introduces the concept of performance-based limits for process control and presents basic guidelines for proper sampling. In addition, the principles for interpreting control charts are presented. Prerequisites : This lesson is designed for participants who are familiar with the basics of statistical process control, the basic components of control charts, the characteristics of a normal curve, and control charts for variables.
Description : This lesson discusses principles of attribute control charts and shows how to plot and interpret various control charts for attributes, including p, np, u, c, and multiple characteristic charts. Prerequisites : This lesson is designed for participants familiar with the principles of statistical process control as well as control charts for variables and attributes.
Explain the meaning of process capability and the process capability index. Explain the term Six Sigma. Explain the process of acceptance sampling and describe the use of operating characteristic OC curves. Describe the challenges inherent in measuring quality in service organizations. Lecture on Statistical Quality Control.
Description of statistical quality control and control charts. Resource providing information in order to understand the purpose and function of statistical quality control, understand the differences between attributes and variables, and become familiar with basic methods of statistical process control.
Rresource covering what is meant by the term statistical quality control, why different types of control charts are necessary, how to construct and interpret a small variety of control charts, in particular those based on means and ranges, and how to describe in outline the relationship between hypothesis testing and statistical quality control. Use and interpretation of statistical quality control charts.