[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-563311-105":53,"doc-detail-563311-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","control-charts-learn-to-select-and-interpret-control-charts-99-confidence","CONTROL CHARTS - Learn to Select and Interpret Control Charts - 99% Confidence","","CONTROL CHARTS summarizes how to choose, maintain, and interpret control charts to detect “special causes” of process variation. It explains how control charts extend run charts with statistically calculated confidence intervals (commonly using 99% confidence) to flag points outside the band as significantly different from random chance. The guide details construction steps: select the metric, choose the right chart, gather enough data, plot results, and draw UCL/LCL limits. It also presents formulas for X̄-R charts and a table linking subgroup size to constants for calculating control limits.",{"@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":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/control-charts-learn-to-select-and-interpret-control-charts-99-confidence/563311/",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-charts-learn-to-select-and-interpret-control-charts-99-confidence/563311.png","ImageObject",442,249,{"name":88,"@type":89},"Taylor Morgan","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-27","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},"How do I select the correct control chart for an application?","Question",{"text":109,"@type":110},"Select the metric to be evaluated first, then choose the right control chart for that metric and gather enough data to compute the control limits.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What qualifies as a “special cause” in a control chart?",{"text":114,"@type":110},"A special cause is indicated when a point falls outside the confidence interval band, meaning it has a low probability of occurring by random chance and is significantly different.",{"name":116,"@type":107,"acceptedAnswer":117},"How are control limits calculated for an X̄-R chart?",{"text":118,"@type":110},"For the averages chart, CL is X̄ with UCL = X̄ + A2·R and LCL = X̄ − A2·R; for the range chart, CL is R with UCL = D4·R and LCL = D3·R, using constants based on subgroup size n.","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},563311,1792023998,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"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":25,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":47},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","CONTROL CHARTS  \nLearning Objectives  \n• Understand how to select the correct control chart for an application.  \n• Know how to fill out and maintain a control chart.  \n• Know how to interpret a control chart to determine the occurrence of“special causes” of variation.  \nHow does it help?  \nControl charts are useful to:  \n•determine the occurrence of“special cause” situations.  \n•Utilize the opportunities presented by “special cause” situations” to identify and correct the occurrence of the “special causes”.  \nKEYS TO SUCCESS  \nUse control charts on only a few critical output characteristics Ensure that you have the means to investigate any “special cause”  \nWhat is a “Special Cause”?  \nRemember our earlier work with confidence intervals? Any occurrence which falls outside the confidence interval has a low probability of occurring by random chance and therefore is “significantly different”. If we can identify and correct the cause, we have an opportunity to significantly improve the stability of the process. Due to the amount of data involved, control charts have historically used 99% confidence for determining the occurrence of these “special causes”  \nSpecial cause occurrence.  \nPoint Value  \nX  \n+/-3s = 99% Confidence Band  \nWhat is a Control Chart ?  \nA control chart is simply a run chart with confidence intervals calculated and drawn in. These “Statistical control limits” form the trip wires which enable us to determine when a process characteristic is operating under the influence of a “Special cause”.  \n+/-3s = 99% Confidence  \nInterval  \nSo how do I construct a control chart?  \nFirst things first:  \n•Select the metric to be evaluated  \n•Select the right control chart for the metric  \n•Gather enough data to calculate the control limits  \n•Plot the data on the chart  \n•Draw the control limits (UCL & LCL) onto the chart.  \n•Continue the run, investigating and correcting the cause of any “out of control” occurrence.  \nHow do I calculate the control limits? X − R Chart  \nFor the averages chart: CL = X  \nUCL = X + A2 R  \nLCL = X − A2 R  \nFor the range chart: CL = R  \nUCL = D4 RLCL = D 3 R  \n| n | D4 | D3 | A2 |\n| --- | --- | --- | --- |\n| 2 | 3.27 | 0 | 1.88 |\n| 3 | 2.57 | 0 | 1.02 |\n| 4 | 2.28 | 0 | 0.73 |\n| 5 | 2.11 | 0 | 0.58 |\n| 6 | 2.00 | 0 | 0.48 |\n| 7 | 1.92 | 0.08 | 0.42 |\n| 8 | 1.86 | 0.14 | 0.37 |\n| 9 | 1.82 | 0.18 | 0.34 |\n\nX = average of the subgroup averages R = average of the subgroup range values A 2 = a constant function of subgroup size (n)  \nUCL = upper control limit LCL = lower control limit","cbCaif6riEOH01oN","https://ap.wps.com/l/cbCaif6riEOH01oN","pdf",114706,"English","# Learning Objectives\n# How Does It Help?\n# Keys to Success\n# What Is a “Special Cause”?\n# What Is a Control Chart?\n# How Do I Construct a Control Chart?\n# How Do I Calculate the Control Limits?\n## X-R Chart Formulas","[{\"question\":\"How do I select the correct control chart for an application?\",\"answer\":\"Select the metric to be evaluated first, then choose the right control chart for that metric and gather enough data to compute the control limits.\"},{\"question\":\"What qualifies as a “special cause” in a control chart?\",\"answer\":\"A special cause is indicated when a point falls outside the confidence interval band, meaning it has a low probability of occurring by random chance and is significantly different.\"},{\"question\":\"How are control limits calculated for an X̄-R chart?\",\"answer\":\"For the averages chart, CL is X̄ with UCL = X̄ + A2·R and LCL = X̄ − A2·R; for the range chart, CL is R with UCL = D4·R and LCL = D3·R, using constants based on subgroup size n.\"}]","CONTROL CHARTS - Learn to Select and Interpret Control Charts - 99% Confidence | PDF",1791828613]