【導讀】中國最大的管理資料下載中心?！?Sigma項目運作實例》。項目定義是由冠軍來完成的。我們簡單介紹以下項目是如何定義的。1確定主要商業(yè)問題：。b質量/缺陷水平。a選擇項目的工具。b3項目完成周期為4個月。b4最少的資金總額。b5黑帶的第一個項目必須滿足培訓目標。我們在定義階段需要做什么？2，完成項目預測節(jié)省金額。6，完成目標陳述。7，組成項目小組，列出小組成員。8，完成財務評估。分六個方面進行問題陳述：?！?Sigma項目運作實例》->《定義階段》->如何繪制宏觀圖。4，小組人員不超過5人。工程部門-工藝，產品，設計及設備。生產部門-操作員，各班次主管，培訓員，操作班長，維修技師。工程標準，工作指示。加入并標明“隱形工廠”工段。標明為VA或NVA，標明可能消除的步驟。加入DUP，RTY，COPQ，循環(huán)周期等估計值。標準上下限和標準控制文件。應使用最新的控制文件。輸入指標標準，若存在。7，確認流程圖準確性。8，必要時更改及更新流程。標明未記錄的Y和可控的X

　　

【正文】 For review: What is the focus of Six Sigma? Q. What does this equation represent? A. A mathematical model of a process Purpose of Regression: to predict Y from a setting of x Examples: Distance = f(acceleration, initial velocity, time) 中國最大的管 理 資料下載中心 (收集 \整理 . 大量免費資源共享 ) 第 21 頁 共 67 頁 Product yield = f(concentrations of reactants) Hardness = f(alloy, anneal temperature) ) ( x f Y = Remember, the focus of Six Sigma is to determine the defining equation of the process. It is to identify the important input variables, determine the relationship to the outputs, determine the optimum values of the critical inputs and then control the inputs at the optimum settings. To do this, the Black Belt must know the relationship between the inputs and the outputs. This module discusses linear modeling techniques for identifying the relationship between continuous variable inputs and continuous variable outputs. A Simple Linear Model Linear equations require continuous input and output variables. One other assumption is that the independent variable (input) is known and fixed and that all of the variation is in the dependent variable (output). This is not usually the case, but often the inputs are settings on dials or gauges or software that seems fixed and invariable. Many times the variation in the output is a function of the inability of the input controller to hold the input at the same value. Collecting Data (y amp。 x) – A Few Thoughts Pg 8 ?March 01, Breakthrough Management Group. Unpublished proprietary work available only under license. All rights reserved. March 16, 2020 Make sure the process settings cover the likely production range (but not too far). Too great a range points outside the normal range may have too great an effect on the model. Too small a range Error term may dominate the fit. Take several replicates at each input setting (x). Replicate runs help increase the model accuracy. Randomize runs whenever practical. Run order is often significant factor. The output (y) at different inputs (x 抯 ) is not always independent of previous settings. 中國最大的管 理 資料下載中心 (收集 \整理 . 大量免費資源共享 ) 第 22 頁 共 67 頁 A good spread in the data is required for a good model. Consider two examples: All of the data is collected at the normal process settings. In this case, regression will try to fit a linear model to a bination of random process variation and random measurement variation. The results will be of no value. The second case is when most of the data is clustered around the standard settings except for a couple of points at the extreme ranges. In this case, the extreme points control the fit of the model. If one of the extreme points is a flyer, then the model will be in error due to the flyer. The ideal case is for the Black Belt to collect a range of data throughout the process space. 置信區(qū)間 : Confidence Intervals A population is the set of all measurements of interest to the experimenter A sample is a subset of measurements selected from the population An inference is a statement about a population parameter based on information contained in a sample Two types of inference Estimation  A poll has been devised to determine the public’s reaction to a new political scandal. The purpose is to estimate the reaction of all Americans by polling a representative sample Hypothesis testing A vaccine for Lyme disease has been developed but the rate of negative side effects is %. A new vaccine has been developed and it is desired to know if the rate of negative side effects is lower than %. The other branch of statistics is descriptive. Its purpose is merely to describe a set of measurements. Inferential statistics is used to guess what God knows about a population from a sample. Within inferential statistics, there are two types: estimation and hypothesis testing. Estimation is trying to guess the population 中國最大的管 理 資料下載中心 (收集 \整理 . 大量免費資源共享 ) 第 23 頁 共 67 頁 statistics from a sample. Hypothesis testing concerns evaluating a sample statistic and paring it to some hypothetical value. Estimates and the CLT What is the best estimate of the population mean using sample data? The sample mean! How good of an estimate is the sample mean? What factors influence the accuracy of the estimate of the mean from sample data? Recall that: The variation in the distribution of sample means is a function of the variance of the Population and the sample size! n Pop X /σ σ = What About Small Samples? If the population standard deviation is known (it almost never is) use the previous formula for small samples, too If the population sigma is unknown (it usually is): The estimate for standard deviation (s) is used The tdistribution is used instead of the normal (Z) distribution Q: What is a tdistribution? The tdistribution is a family of bellshaped (normallike) distributions that are dependent on sample size The smaller the sample size n, the wider and flatter the distribution n s t X μ n s t X n n 1 , 2 / 1 , 2 / +≤≤α α The tdistribution is the general case for any sample where the population standard deviation is unknown. However, with large samples, the t and zdistributions are nearly identical, so either can be used. You can verify this in Minitab by generating a large sample of normal data and then analyzing it with