6西格玛回归分析

Click to edit Master title style,Click to edit Master text styles,Second level,Third level,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,Cummins Confidential,*,Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,Cummins Confidential,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,Cummins Confidential,*,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,Cummins Confidential,*,回归分析,Regression Analysis,目的,Objectives,介绍相关性及回归的基本概念,Introduce The Basic Concepts of Correlation and Regression,把回归与六西格玛路线图结合起来,Link Regression To The Six Sigma Roadmap,学习多元回归的使用,Review the use of Multiple Regression,介绍相关性及回归的基本概念,Introduce The Basic Concepts of Correlation and Regression,把回归与六西格玛路线图结合起来,Link Regression To The Six Sigma Roadmap,学习多元回归的使用,Review the use of Multiple Regression,介绍相关性及回归的基本概念,Introduce The Basic Concepts of Correlation and Regression,把回归与六西格玛路线图结合起来,Link Regression To The Six Sigma Roadmap,学习多元回归的使用,Review the use of Multiple Regression,项目跟踪图,第五版,项目开始日期,21/01/2004,项目类别,“,Y,”,变量数据,采集计划,制定项目,日程,启动项目书,DMAIC,改善,定义,确定,”,Y,”,变量和,起草项目书,项目书,得以批准,流程图,C&E,矩阵或,故障树分析,FTA,第三十天,MBB,审阅,FMEA,或,故障树分析,FTA,测量系统分析,MSA,关键,”,X,”,变量,数据采集计划,MBB,审阅,测量,21/01/2004,04/02/2004,11/02/2004,25/02/2004,09/03/2004,09/03/2004,09/03/2004,初始能力研究,多元变量流程分析,MBB,审阅,合同批准,分析,22/03/2004,15/04/2004,15/04/2004,15/04/2004,15/04/2004,单因子或多因子测试,实验设计,(DOE),MBB,审阅,改善,31/05/2004,31/05/2004,31/05/2004,控制计划,最终能力研究,控制阶段,FMEA,回顾,重新修订,RPN,MBB,审阅,项目最终汇报,及报告,项目审核,及项目收尾,控制,21/06/2004,29/06/2004,29/06/2004,05/07/2004,09/07/2004,09/07/2004,19/07/2004,(,根据需要使用,),客户心声,/,业务之声调查,VOC/VOB,需求分析,流程再造,解决方案设计,流程再造,在这里输入开始日期,确定改善方案,由项目发起人在备选项目数据库中完成,在,6,西格玛,数据库,查找相似项目,实施改善,移交培训,/,流程所有人签准,再造路线图的日程是独立计算的,与以上,DMAIC,的日期不相关,实际完成日期,计划完成日期,图例,2/1/02002,2/3/02,完成,画钩,分析路线图,Analyze Roadmap,单一因子,X -,单一因子,Y,Single X - Single Y,输入变量,X,X Data,离散,Discrete,连续,Continuous,输出变量,Y,Y Data,离散,Discrete,连续,Continuous,卡方相关性分析,Chi-Square,逻辑回归,Logistic,Regression,方差分析，,均值,/,中位数测试,ANOVA,Means / Medians Tests,回归,Regression,什么是,Y ?,_,数据类型,?,_,什么是,X ?,_,数据类型,? _,应该使用何种工具,?,_,案例,#1,Scenario #1,管理者想知道接线员的经验,(,以月为单位衡量,),是否会对接听顾客热线电话需要的时间有影响,相关性,Correlation,什么是相关性,?,What is correlation?,你是否有过如此经验,:,测量某些产品并送至顾客处,，但他们回来告诉你的产品不符规格,?,Have you ever measured something and then shipped to your customer only for them to tell you it doesnt meet spec?,在奥林匹克溜冰比赛上，你认为两个裁判成绩之相关性有多高,?,How well correlated do you think two ice skating judges are at the Olympics?,相关性,Correlation,路线分析图,Analyze Roadmap,画出点阵图,Produce Scatter Plot,计算相关性,Calculate Correlation,评估,r,和,P,值,Evaluate r,and P value,相关系数,Correlation Coefficients,什么是相关系数,?,So what is the Correlation Coefficient supposed to be anyway?,相关系数,(r),介于,-1,和,1,之间,The Correlation Coefficient (r) lies between -1 and 1,一般规则,:,General Rules,相关系数,(r) .80,或, .80 or BASIC STATS CORRELATION.,不选择,p,值选项,Uncheck p value,年度和市场营销费用有着很高的相关性！我们必须只能选择一个作为预测变量在回归拟合中使用市场营销费用可能受年度影响，因此我们保留市场营销费用，而去掉年度变量,The Year and Marketing$ Variables are highly correlated! We will have to choose one or the other of the correlated predictor variables (but not both) to use in a regression fit.,Possible that marketing$ is a function of the year - so keep the,marketing $,and eliminate,year,.,基本原则,如果相关性,0.8 or, Regression Best Subsets.,最佳子集回归：刹车板销售,注意,”,年度,”,从模式中去掉了,.,Best Subsets Regression: Sales versus Mktg$, Sales Rep, .,Response is Sales,S L,a Y P,l ( r,M e S o,k s a d,t l u,g R e c,Vars R-Sq R-Sq(adj) C-p S $ e s t,1 79.0 77.8 156.0 12.841 X,1 20.9 16.6 631.3 24.910 X,2 90.1 89.0 66.8 9.0570 X X,2 85.2 83.5 107.0 11.084 X X,3 98.2 97.8 3.0 4.0222 X X X,3 90.5 88.7 65.8 9.1570 X X X,4 98.2 97.7 5.0 4.1540 X X X X,多元回归,Multiple Regression,路线分析图,规划分析内容,收集数据,利用回归或最佳子集分析,Analyze Using Regression or Best Subsets,评估残差,制定决策,评估,R,2,及,P,值的显著性,多,元共线性,分析（相关性）,Multicollinearity “X” Check (correlation),使用多元回归简化模式,Run Multiple Regression Reduced Model,因为有多条线，,就不再使用拟合线图，,No longer fitted,line plot due to,multiple lines,回归,:,刹车板销售,Regression: Brake Sales,选择所有四个预测变量和响应变量,.,Select all four predictor variables and the response variable.,使用,Minitab,菜单,STAT Regression Regression,回归分析,:,刹车板销售,Regression Analysis: Brake Sales,零假设,=,变量间没有任何关系,备择假设,=,变量间有一些关系,Ho = No relationship between variables,Ha = Some relationship exists between variables,Regression Analysis: Sales versus Mktg$, Sales Rep, .,The regression equation is,Sales = - 66.6 + 11.8 Mktg$ + 1.18 Sales Rep + 2.70 LY(SalesRep),- 0.007 Product,Predictor Coef SE Coef T P,Constant -66.64 19.17 -3.48 0.003,Mktg$ 11.838 1.494 7.92 0.000 Ha,Sales Re 1.1751 0.1224 9.60 0.000Ha,LY(Sales 2.7023 0.1154 23.42 0.000Ha,Product -0.0068 0.2337 -0.03 0.977Ho,S = 4.154 R-Sq = 98.2%,R-Sq(adj) = 97.7%,回归,/,简化模式,:,刹车板销售,Regression/Reduced Model: