[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-657298-105":59,"doc-detail-657298-en":124},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":117,"head_meta":119,"extra_data":121,"updated_unix":123},105,"en","qi-essentials-toolkit-scatter-diagram-use-for-correlation-analysis","QI Essentials Toolkit - Scatter Diagram - use for correlation analysis","","QI Essentials Toolkit’s Scatter Diagram overview explains how to visualize the relationship between two variables using X- and Y-axes. It guides teams through gathering paired data, assigning the suspected cause to the horizontal axis and the suspected effect to the vertical axis, labeling ranges, plotting points, and interpreting resulting patterns. The material emphasizes that scatter diagrams reveal correlation but do not prove causation, and includes an applied example with a described positive correlation.",{"@graph":69,"@context":116},[70,84,99],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/qi-essentials-toolkit-scatter-diagram-use-for-correlation-analysis/657298/",{"url":83,"name":65,"@type":85,"author":86,"headline":65,"publisher":89,"fileFormat":92,"inLanguage":63,"description":67,"dateModified":93,"datePublished":93,"encodingFormat":92,"isAccessibleForFree":94,"interactionStatistic":95},"DigitalDocument",{"name":87,"@type":88},"DoDO","Person",{"url":74,"name":90,"@type":91},"DocShare","Organization","application/pdf","2026-10-26",true,{"@type":96,"interactionType":97,"userInteractionCount":4},"InteractionCounter",{"@type":98},"ViewAction",{"@type":100,"mainEntity":101},"FAQPage",[102,108,112],{"name":103,"@type":104,"acceptedAnswer":105},"What does a scatter diagram show about two variables?","Question",{"text":106,"@type":107},"A scatter diagram shows whether two variables are correlated. The pattern formed by data points can suggest a positive, negative, or little/none relationship.","Answer",{"name":109,"@type":104,"acceptedAnswer":110},"How should you choose the X-axis and Y-axis variables?",{"text":111,"@type":107},"Plot the suspected cause on the horizontal (X) axis and the suspected effect on the vertical (Y) axis, keeping each data pair matched.",{"name":113,"@type":104,"acceptedAnswer":114},"Does a scatter diagram prove causation?",{"text":115,"@type":107},"No. It indicates correlation between two variables but does not prove causation.","https://schema.org",{"og:url":83,"og:type":118,"og:title":65,"og:site_name":90,"og:description":67},"article",{"robots":120,"canonical":83},"index,follow",{"doc_id":122,"site_id":62},657298,1792977070,{"code":4,"msg":5,"data":125},{"doc_id":122,"user_id":126,"nickname":87,"user_avatar":127,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":128,"file_id":129,"file_url":130,"file_type":131,"file_size":132,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":133,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":67,"update_tm":123,"read_time":52},962093270745,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","QI Essentials Toolkit:  \nScatter Diagram  \nA scatter diagram—also known as a “scatter plot” —is a graphic representation of the relationship between two variables. Teams use scatter diagrams to help them identify cause-and-effect relationships between two variables. For example, after brainstorming multiple causes and effects using a fishbone (cause and effect) diagram, your team might use a scatter diagram to determine whether a particular cause and effect are related.  \nA scatter diagram graphs one variable on the X-axis and the other variable on the Y-axis. If the two variables are related, the data points will fall along a diagonal line or curve—that is, values for variable X will be associated with values for variable Y. This could show either a positive correlation (values for both variables increase or decrease) or a negative correlation (values for one variable increase while those for the other decrease) .  \nNote: A scatter diagram shows a correlation between two variables; it does not prove causation.  \n\n| IHI’s QI Essentials Toolkit includes the tools and templates you need to launch and manage a successful improvement project. Each of the nine tools in the toolkit includes a short description, instructions, an example, anda blank template. NOTE: Before filling out the template, first save the file on your computer. Then open and use that version of the tool. Otherwise, your changes will not be saved. |  |  |\n| --- | --- | --- |\n| 􀁸 Cause and Effect Diagram\u003Cbr>􀁸 Driver Diagram\u003Cbr>􀁸 Failure Modes and Effects Analysis (FMEA) | 􀁸 Flowchart\u003Cbr>􀁸 Histogram\u003Cbr>􀁸 Pareto Chart\u003Cbr>􀁸 PDSA Worksheet | 􀁸 Project Planning Form\u003Cbr>􀁸 Run Chart & ControlChart\u003Cbr>􀁸 Scatter Diagram |\n\nCopyright © 2017 Institute for Healthcare Improvement. All rights reserved. Individuals may photocopy these materials for educational, not-for-profit uses, provided that the contents are not altered in any way and that proper attribution is given to IHI as the source of the content. These materials may not be reproduced for commercial, for-profit use in any form or by any means, or republished under any circumstances, without the written permission of the Institute for Healthcare Improvement.  \nInstructions  \n1) Gather the data foryour two variables. On the data collection sheet, put each data pair in their respective columns. Ensure the data remain paired.  \n2) Decide which factor you will plot on which axis. Put the suspected cause on the horizontal (X) axis, and the suspected effect on the vertical (Y) axis.  \n3) Draw and label the axes clearly.  \no Make the axes roughly the same length, creating a square plotting area.  \no Label each axis with increasing values from left to right, and from bottom to top.  \no Label each axis to match the full range of values for that factor. In other words, make the lowest numerical label slightly less than the lowest data value, and the highest label slightly greater than the highest value. The data should fill the whole plotting area.  \n4) PlottheX andY data pairs on the diagram; place a point on the graph where the two values intersect.  \n5) Include a title atthe top of the diagram and provide necessary annotations to describe what it shows.  \n6) Study the pattern that the plotted data points create to identify what you have learned and decide on your next steps. If there is an association between the two variables, the pattern will resemble an elliptical shape or a straight line.  \no If the points for both values are increasing (line slants from lower left to upper right): The correlation is positive.  \no If one variable increases while the other decreases (line slants from upper left to lower right): The correlation is negative.  \no If the points form a circular shape: There is little or no correlation.  \nExample: Scatter Diagram  \nThis example scatter diagram shows the relationship between median income (X-axis) and Metro Health Index, a measure of people who smoke or are obese (Y-axis) . The researcher wanted ","cbCaiuiRTfiNr79H","https://ap.wps.com/l/cbCaiuiRTfiNr79H","pdf",530983,"English","# Scatter Diagram\n## Purpose and interpretation\n## How to create the diagram\n## Example and correlation findings\n## Template: Data Collection Sheet\n## Template: Scatter Diagram","[{\"question\":\"What does a scatter diagram show about two variables?\",\"answer\":\"A scatter diagram shows whether two variables are correlated. The pattern formed by data points can suggest a positive, negative, or little/none relationship.\"},{\"question\":\"How should you choose the X-axis and Y-axis variables?\",\"answer\":\"Plot the suspected cause on the horizontal (X) axis and the suspected effect on the vertical (Y) axis, keeping each data pair matched.\"},{\"question\":\"Does a scatter diagram prove causation?\",\"answer\":\"No. It indicates correlation between two variables but does not prove causation.\"}]","QI Essentials Toolkit - Scatter Diagram - use for correlation analysis | PDF"]