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It explains how to construct and interpret control charts, linking normal distribution and key statistical measures to practical charting. The guide also covers Shewhart rules for determining whether a process is in statistical control or out of control (OOC), distinguishing common-cause variation from special-cause variation, and clarifying the meaning of an out-of-control process.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/control-chart-basics-participant-guide-primary-knowledge-unit/563314/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/control-chart-basics-participant-guide-primary-knowledge-unit/563314.png","ImageObject",442,249,{"name":88,"@type":89},"Jordan Avery","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-20","2026-10-12",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",6,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What is the purpose of control charts in SPC?","Question",{"text":109,"@type":110},"Control charts graphically represent aspects of a process over time and help engineers and technicians recognize changes quickly. They support identifying whether variation is due to common or special causes.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"How do you distinguish controlled (in-control) variation from uncontrolled (out-of-control) variation?",{"text":114,"@type":110},"Controlled variation is predictable and follows a consistent probabilistic pattern associated with common causes. Uncontrolled variation is unpredictable and typically indicates a special or assignable cause that must be identified and corrected.",{"name":116,"@type":107,"acceptedAnswer":117},"What role does normal distribution play in understanding control charts?",{"text":118,"@type":110},"Normal distribution helps describe how process data is distributed and why many control-chart methods assume a bell-shaped pattern. The guide links standard deviations from the mean to expected percentages of data.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},563314,1792290504,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":142},1099523882367,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Control Chart Basics  \nPrimary Knowledge Unit  \nParticipant Guide  \nDescription and Estimated Time to Complete  \nControl Charts are one of the primary tools used in Statistical Process Control or SPC. Control charts help to graphically represent different aspects of a process and to assist engineers and technicians with producing a quality product. In this unit you learn the basics of control charts , how they are constructed, and how they are interpreted.  \nEstimated Time to Complete  \nAllow approximately 30 minutes to read through this unit.  \nUnit and Related Activity Objective(s) / Outcomes  \nObjectives    \n• Explain normal distribution and how it relates to 􀀡 -charts .  \n• Given a set of data, construct an 􀀡 -chart.  \n• Interpret control charts by applying the Shewhart rules to identify processes that are Out of Control (OOC) .  \nOutcomes  \nYou should be able to construct a basic 􀀡 -chart and apply Shewhart rules to interpret a control chart. You should also be able to explain what is meant by an out of control process.  \nTerminology  \nAttribute Data Control Chart Mean Median  \nNormal Distribution  \nStandard Deviation Target  \nVariable Data  \nVariance  \nSouthwest Center for Microsystems Education (SCME) Page 1 of 24  \nSPC_PK02_PG_July2017.docx Control Chart Basics PK  \nIntroduction to Control Charts  \nA Bell Laboratories engineer named Dr . Walter A. Shewhart introduced the basic concept of control charts in the 1920’s. Shewhart was able to identify the importance of the reduction of variation in a process . In order to achieve continuous improvement he stressed the necessity of understanding and reducing this variation. He introduced a control chart as a tool to bring a process into a state of statistical control. These charts are sometimes referred to as Shewhart Charts .  \nControl charts characterize process variation over time and distinguish between common or inherent cause variation and special cause variation . Common cause variation is usually due to inherent elements of the process which is often referred to as background noise . It is variation that is expected and predictable in a process . Special cause variation is different. It is usually due to some special or assignable cause , something that is not expected or predictable . Control charts show what the process is doing and, when the process changes, show very quickly that a change has occurred.  \nShewhart defined two types of variation, controlled and uncontrolled. Controlled variation is predictable in a probabilistic sense and can be represented by a consistent pattern that can be attributed to common causes or that background noise (e.g., small fluctuation in temperatures, different operators/technicians, different equipment running the same process, process that don’t repeat “exactly”) . If this variation is controlled, then the process can be said to be in control. A process, which is in control, has a predictable or expected variation with no abnormal or deviant data values, nor any consistent trends away from the normal variation . A process, which is in control, has no surprise data values or sets of data values (trends or patterns) . Based on what we know at this point, the process on the right, looks in-control. There are no trends, patterns or extreme highs or lows .  \nUncontrolled variation is when the variation within a process becomes unpredictable , due to a special cause . The special cause of such variation needs to be identified and corrected in order to remove this uncontrolled variation from a process . A control chart is a helpful tool in characterizing the variation for a process that is already in control, and for identifying when a process is no longer in control, or in other words, out of control (OOC) .  \nSouthwest Center for Microsystems Education (SCME) Page 2 of 24  \nSPC_PK02_PG_July2017.docx Control Chart Basics PK  \nUnderstanding the basic mathematical concepts involved with SPC is important in developing and utilizing contr","cbCais4YiupyC9ja","https://ap.wps.com/l/cbCais4YiupyC9ja","pdf",2140551,24,"English","# Introduction to Control Charts\n## What control charts do in SPC\n## Controlled vs uncontrolled variation\n# Understanding the Basic Mathematical Concepts\n## Sample statistics for charting\n# Normal Distribution\n## Gaussian and bell-shaped properties","[{\"question\":\"What is the purpose of control charts in SPC?\",\"answer\":\"Control charts graphically represent aspects of a process over time and help engineers and technicians recognize changes quickly. They support identifying whether variation is due to common or special causes.\"},{\"question\":\"How do you distinguish controlled (in-control) variation from uncontrolled (out-of-control) variation?\",\"answer\":\"Controlled variation is predictable and follows a consistent probabilistic pattern associated with common causes. Uncontrolled variation is unpredictable and typically indicates a special or assignable cause that must be identified and corrected.\"},{\"question\":\"What role does normal distribution play in understanding control charts?\",\"answer\":\"Normal distribution helps describe how process data is distributed and why many control-chart methods assume a bell-shaped pattern. The guide links standard deviations from the mean to expected percentages of data.\"}]","Control Chart Basics - Participant Guide - Primary Knowledge Unit | PDF",1791828616,8]