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Related Concept Videos

Interpreting Run Charts01:25

Interpreting Run Charts

2.9K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
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Pareto Chart00:52

Pareto Chart

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A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
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The X̄ Chart00:58

The X̄ Chart

292
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
292
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

164
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
164
Interpreting R Charts01:22

Interpreting R Charts

179
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Related Experiment Video

Updated: Nov 7, 2025

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
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Using control charts to understand community variation in COVID-19.

Moira Inkelas1,2, Cheríe Blair3, Daisuke Furukawa3

  • 1Department of Health Policy and Management, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.

Plos One
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

Statistical process control offers interpretable COVID-19 data displays for local pandemic response. These control charts reveal regional variations, aiding decision-makers in timely mitigation and containment strategies.

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Area of Science:

  • Public Health
  • Epidemiology
  • Statistical Analysis

Background:

  • The COVID-19 pandemic necessitates timely data for effective decision-making.
  • Existing data presentation methods may lack the granularity needed for local-level interventions.
  • Statistical process control (SPC) is widely used in industries to monitor and manage process variations.

Purpose of the Study:

  • To demonstrate a novel application of SPC for real-time COVID-19 data visualization.
  • To provide interpretable displays that inform local mitigation and containment strategies.
  • To highlight the value of SPC for policy-makers and communities during the pandemic.

Main Methods:

  • Developed control charts at county and city/neighborhood levels in California.
  • Utilized SPC principles to analyze and disaggregate COVID-19 data.
  • Annotated time series presentations to link events and policies with data trends.

Main Results:

  • COVID-19 rates exhibit significant regional and sub-regional variations.
  • Identified periods of both exponential and non-exponential growth and decline in disease rates.
  • Demonstrated that disaggregated data provides actionable granularity for decision-makers.

Conclusions:

  • Control charts offer a valuable tool for real-time decision-making in public health.
  • SPC facilitates interpretable communication of pandemic data to communities.
  • This approach can effectively mobilize and direct stakeholder actions for pandemic response.