【導(dǎo)讀】《6Sigma項(xiàng)目運(yùn)作實(shí)例》->《定義階段》->如何項(xiàng)目定義一個(gè)項(xiàng)目。我們簡(jiǎn)單介紹以下項(xiàng)目是如何定義的。1確定主要商業(yè)問(wèn)題：。2對(duì)與生產(chǎn)來(lái)說(shuō)：。b質(zhì)量/缺陷水平。a選擇項(xiàng)目的工具。b3項(xiàng)目完成周期為4個(gè)月。b4最少的資金總額。b5黑帶的第一個(gè)項(xiàng)目必須滿足培訓(xùn)目標(biāo)。我們?cè)诙x階段需要做什么？6，完成目標(biāo)陳述。7，組成項(xiàng)目小組，列出小組成員。8，完成財(cái)務(wù)評(píng)估。分六個(gè)方面進(jìn)行問(wèn)題陳述：。4，小組人員不超過(guò)5人。生產(chǎn)部門-操作員，各班次主管，培訓(xùn)員，操作班長(zhǎng)，維修技師。工程標(biāo)準(zhǔn)，工作指示。標(biāo)示各工序標(biāo)準(zhǔn)控制文件。加入并標(biāo)明“隱形工廠”工段。標(biāo)明為VA或NVA，標(biāo)明可能消除的步驟。觀察導(dǎo)致行為改變。確認(rèn)實(shí)際工藝設(shè)置與記錄的設(shè)置相同。輸入指標(biāo)標(biāo)準(zhǔn)，若存在。7，確認(rèn)流程圖準(zhǔn)確性。8，必要時(shí)更改及更新流程。加入量具重復(fù)性及復(fù)驗(yàn)性數(shù)據(jù)。隨著對(duì)工藝的深入了解，更新工藝圖以反映新的信息

　　

【正文】 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 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 both the z and t distribution routines. Proportions and Binomial Experiments Pg 35 . April 01, Breakthrough Management Group. Unpublished proprietary work available only under license. All rights reserved. April 3, 2020 Proportion data is usually the result of a binomialtype experiment Binomial experiments (or Bernoulli trials) are those that have only one of two outes, either a “ success” or a “ failure” The probability of this type of experiment is described by a binomial distribution, a plicated distribution In many cases the normal distribution can be used to approximate the binomial distribution When nxp 5 and nx(1p)5  μ = nxp and σ 2 = nxpx(1p) Binomial distributions are discussed in almost every statistics textbook. Calculations with them is not necessarily difficult, but it is tedious if it must be done manually. Minitab has routines, however, that greatly simplifies the calculations. If the binomial approximation applies and the data can be estimated with a normal distribution other statistical tests and control charts can be used that would not be available otherwise. Try to construct your experiments such that the binomial approximation is valid. A general rule of thumb: for the normal approximation to apply, have a sample size of at least 30 and large enough to guarantees at least 5 successes. 假設(shè)測(cè)試 : Introduction to Hypothesis Testing A Bright Idea Notes: Pg 5 11 Nov 2020 ?April 01, Breakthrough Management Group. Unpublished proprietary work available only under license. All rights reserved. A light bulb pany is trying to produce a brighter light bulb for the same energy. It is hoped that a change in the filament coating process will produce a brighter light. The engineer collected the last ten light bulbs made before the process change and the first ten after the change. The mean light output of the old process bulbs is 1251 lumens and the new process is 1273 lumens. Does the increase of 22 in the means of the two groups represent a real improvement? Could the difference between these two groups have happened by random chance? Should the engineer switch to the new process? These kinds of problems are very familiar to engineers. An engineer is given a task to improve a process or product. After a change in the process, the engineer is left with the problem of determining whether the process change has made a significant improvement or not. Though engineers often use more advanced techniques to determine the improved settings (DOE, for example, to be discussed later), a hypothesis test is often used to verify the experiment results. The process may be as follows: ?Identify the problem. ?Design and run an experiment to find an improved condition. ?Analyze the data and determine the improved operating point. ?Verify the effectiveness of the improvement with a hypothesis test. A Few Illuminating Details QC data were available for light bulbs produced in the same factory. All of the bulbs had been produced using the standard filament coating process. The data was prised of the averages of 10 samples from consecutive batches of light bulbs. The engineer calculated the differences between consecutive groups and recorded it in a Minitab worksheet in column QCData. Considering the new data, the question now bees: “ How often has the mean brightness of a group of light bulbs been 22 lumens brighter than the group produced immediately before?” Open Minitab worksheet for the data In the Minitab datasheet, column ‘ OldData’ is the first set of 10 from the old process. ‘ NewData’ is the set of data from the new process. ‘ PlantData’ is the 210 averages of 10 consecutive measurements from the old process in sample order. ‘ Diff’ is the magnitude of the difference between consecutive groups. What Is a Hypothesis Test? A hypothesis test is simply paring reality to an assumption and asking, “ Are they the same?” Or A hypothesis test is testing wheth