6西格玛绿带培训MaterialsTWO

1、硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Revision: 1.00Date: June 20016 6西格玛绿带培训西格玛绿带培训MaterialsMaterialsTWOTWO-6-4-20246硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 第二天: Tests of Hypotheses- Week 1 recap of Statistics Terminology - Introduction to Student T distribution- Example in using Student T di

2、stribution- Summary of formula for Confidence Limits- Introduction to Hypothesis Testing- The elements of Hypothesis Testing-Break- Large sample Test of Hypothesis about a population mean- p-Values, the observed significance levels- Small sample Test of Hypothesis about a population mean- Measuring

3、the power of hypothesis testing- Calculating Type II Error probabilities- Hypothesis Exercise I-Lunch- Hypothesis Exercise I Presentation- Comparing 2 population Means: Independent Sampling- Comparing 2 population Means: Paired Difference Experiments- Comparing 2 population Proportions: F-Test-Break

4、- Hypothesis Testing Exercise II (paper clip)- Hypothesis Testing Presentation- 第一天wrap up硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 第二天: Analysis of variance 和simple linear regression- Chi-square : A test of independence- Chi-square : Inferences about a population variance- Chi-square exercise- ANOV

5、A - Analysis of variance- ANOVA Analysis of variance case study-Break- - Testing the fittness of a probability distribution- Chi-square: a goodness of fit test- The Kolmogorov-Smirnov Test- Goodness of fit exercise using dice- Result 和discussion on exercise-Lunch- Probabilistic 关系hip of a regression

6、 model- Fitting model with least square approach- Assumptions 和variance estimator- Making inference about the slope- Coefficient of Correlation 和Determination- Example of simple linear regression- Simple linear regression exercise (using statapult)-Break- Simple linear regression exercise (cont)- Pr

7、esentation of results- 第二天wrap up硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Day 3: Multiple regression 和model building- Introduction to multiple regression model- Building a model- Fitting the model with least squares approach- Assumptions for model- Usefulness of a model- Analysis of variance- Using

8、 the model for estimation 和prediction- Pitfalls in prediction model-Break- Multiple regression exercise (statapult)- Presentation for multiple regression exercise-Lunch- Qualitative data 和dummy variables- Models with 2 or more quantitative independent variables- Testing the model- Models with one qu

9、alitative independent variable- Comparing slopes 和response curve-Break- Model building example- Stepwise regression an approach to screen out factors- Day 3 wrap up硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Day 4: 设计of Experiment- Overview of Experimental Design- What is a designed experiment- Object

10、ive of experimental 设计和its capability in identifying the effect of factors- One factor at a time (OFAT) versus 设计of experiment (DOE) for modelling- Orthogonality 和its importance to DOE- H和calculation for building simple linear model- Type 和uses of DOE, (i.e. linear screening, linear modelling, 和non-

11、linear modelling)- OFAT versus DOE 和its impact in a screening experiment- Types of screening DOEs-Break- Points to note when conducting DOE- Screening DOE exercise using statapult- Interpretating the screening DOEs result-Lunch- Modelling DOE (Full factoria with interactions)- Interpreting interacti

12、on of factors- Pareto of factors significance- Graphical interpretation of DOE results- 某些rules of thumb in DOE- 实例of Modelling DOE 和its analysis-Break- Modelling DOE exercise with statapult- Target practice 和confirmation run- Day 4 wrap up硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Day 5: Statistical

13、 流程Control- What is Statistical 流程Control- Control chart the voice of the 流程- 流程control versus 流程capability- Types of control chart available 和its application- Observing trends for control chart- Out of Control reaction- Introduction to Xbar R Chart- Xbar R Chart example- Assignable 和Chance causes i

14、n SPC- Rule of thumb for SPC run test-Break- Xbar R Chart exercise (using Dice)- Introduction to Xbar S Chart- Implementing Xbar S Chart- 为什么Xbar S Chart ?- Introduction to Individual Moving Range Chart- Implementing Individual Moving Range Chart- 为什么Xbar S Chart ?-Lunch- Choosing the sub-group- Cho

15、osing the correct sample size- Sampling frequency- Introduction to control charts for attribute data- np Charts, p Charts, c Charts, u Charts-Break- Attribute control chart exercise (paper clip)- Out of control not necessarily is bad- Day 5 wrap up硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Recap of S

16、tatistical TerminologyDistributions differs in locationDistributions differs in spreadDistributions differs in shapeNormal Distribution-6 -5 -4 -3 -2 -1 01 2 3 4 5 6 - 99.9999998% - 99.73% - 95.45% -68.27%- 3 variation is called natural tolerance Area under a Normal Distribution硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴

17、软商品交易在阿里巧巧软商品交易在阿里巧巧 流程流程capability potential, CpBased on the assumptions that :流程is normalNormal Distribution-6 -5 -4 -3 -2 -1 01 2 3 4 5 6 Lower Spec LimitLSLUpper Spec LimitUSLSpecification CenterIt is a 2-sided specification流程mean is centered to the device specificationSpread in specificationNat

18、ural toleranceCP =USL - LSL6 8 6 = 1.33硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 流程流程Capability Index, CpkBased on the assumption that the 流程is normal 和in control2. An index that compare the 流程center with specification centerNormal Distribution-6 -5 -4 -3 -2 -1 01 2 3 4 5 6 Lower Spec LimitLSLUpper

19、Spec LimitUSLSpecification CenterTherefore when ,Cpk 20) Estimated 标准偏差标准偏差, R/d2 Population 标准偏差标准偏差, (when sample size, n 20) 硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Probability TheoryProbability is the chance for an event to occur. Statistical dependence / independence Posterior probability Rel

20、ative frequency Make decision through probability distributions(i.e. Binomial, Poisson, Normal)Central Limit TheoremRegardless the actual distribution of the population, the distribution of the mean for sub-groups of sample from that distribution, will be normally distributed with sample mean approx

21、imately equal to the population mean. Set confidence interval for sample based on normal distribution. A basis to compare samples using normal distribution, hence making statistical comparison of the actual populations. It does not implies that the population is always normally distributed.(Cp, Cpk

22、must always based on the assumption that 流程流程is normal)硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Inferential StatisticsThe 流程流程of interpreting the sample data to draw conclusions about the population from which the sample was taken. Confidence Interval(Determine confidence level for a sampling mean

23、to fluctuate) T-Test 和和F-Test(Determine if the underlying populations is significantly different in terms of the means 和和variations) Chi-Square Test of Independence(Test if the sample proportions are significantly different) Correlation 和和Regression(Determine if 关系关系hip between variables exists, 和和g

24、enerate model equation to predict the outcome of a single output variable)硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Central Limit TheoremThe mean x of the sampling distribution will approximately equal to the population mean regardless of the sample size. The larger the sample size, the closer the s

25、ample mean is towards the population mean.2. The sampling distribution of the mean will approach normality regardless of the actual population distribution.3.It assures us that the sampling distribution of the mean approaches normal as the sample size increases.m = 150Population distributionx = 150S

26、ampling distribution(n = 5)x = 150Sampling distribution(n = 20)x = 150Sampling distribution(n = 30)m = 150Population distributionx = 150Sampling distribution(n = 5)硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 某些某些take aways for sample size 和和sampling distribution For large sample size (i.e. n 30), the

27、sampling distribution of x will approach normality regardless the actual distribution of the sampled population. For small sample size (i.e. n 30), the sampling distribution of x is exactly normal if the sampled population is normal, 和will be approximately normal if the sampled population is also ap

28、proximately normally distributed. The point estimate of population 标准偏差 using S equation may 提供a poor estimation if the sample size is small.Introduction to Student t Distrbution Discovered in 1908 by W.S. Gosset from Guinness Brewery in Ireland. To compensate for 标准偏差 dependence on small sample siz

29、e. Contain two random quantities (x 和S), whereas normal distribution contains only one random quantity (x only) As sample size increases, the t distribution will become closer to that of standard normal distribution (or z distribution).硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Percentiles of the t D

30、istributionWhereby,df = Degree of freedom = n (sample size) 1Shaded area = one-tailed probability of occurencea = 1 Shaded areaApplicable when: Sample size 30 标准偏差 is unknown Population distribution is at least approximately normally distributed0.750.900.950.9750.990.9950.999511.00003.07776.313712.7

31、06231.821063.6559636.577620.81651.88562.92004.30276.96459.925031.599830.76491.63772.35343.18244.54075.840812.924440.74071.53322.13182.77653.74694.60418.610150.72671.47592.01502.57063.36494.03216.868560.71761.43981.94322.44693.14273.70745.958770.71111.41491.89462.36462.99793.49955.408180.70641.39681.

32、85952.30602.89653.35545.041490.70271.38301.83312.26222.82143.24984.7809100.69981.37221.81252.22812.76383.16934.5868110.69741.36341.79592.20102.71813.10584.4369120.69551.35621.78232.17882.68103.05454.3178130.69381.35021.77092.16042.65033.01234.2209140.69241.34501.76132.14482.62452.97684.1403150.69121

33、.34061.75312.13152.60252.94674.0728160.69011.33681.74592.11992.58352.92084.0149170.68921.33341.73962.10982.56692.89823.9651180.68841.33041.73412.10092.55242.87843.9217190.68761.32771.72912.09302.53952.86093.8833200.68701.32531.72472.08602.52802.84533.8496210.68641.32321.72072.07962.51762.83143.81932

34、20.68581.32121.71712.07392.50832.81883.7922230.68531.31951.71392.06872.49992.80733.7676240.68481.31781.71092.06392.49222.79703.7454250.68441.31631.70812.05952.48512.78743.7251260.68401.31501.70562.05552.47862.77873.7067270.68371.31371.70332.05182.47272.77073.6895280.68341.31251.70112.04842.46712.763

35、33.6739290.68301.31141.69912.04522.46202.75643.6595300.68281.31041.69732.04232.45732.75003.6460 0.6741.2821.6451.962.3262.5763.291Area under the curvedf, u ut ( a a, u u )a aArea under the curve硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Percentiles of the Normal Distribution / Z Distribution00.010.02

36、0.030.040.050.060.070.080.0900.50000.50400.50800.51200.51600.51990.52390.52790.53190.53590.10.53980.54380.54780.55170.55570.55960.56360.56750.57140.57530.20.57930.58320.58710.59100.59480.59870.60260.60640.61030.61410.30.61790.62170.62550.62930.63310.63680.64060.64430.64800.65170.40.65540.65910.66280

37、.66640.67000.67360.67720.68080.68440.68790.50.69150.69500.69850.70190.70540.70880.71230.71570.71900.72240.60.72570.72910.73240.73570.73890.74220.74540.74860.75170.75490.70.75800.76110.76420.76730.77040.77340.77640.77940.78230.78520.80.78810.79100.79390.79670.79950.80230.80510.80780.81060.81330.90.81

38、590.81860.82120.82380.82640.82890.83150.83400.83650.838910.84130.84380.84610.84850.85080.85310.85540.85770.85990.86211.10.86430.86650.86860.87080.87290.87490.87700.87900.88100.88301.20.88490.88690.88880.89070.89250.89440.89620.89800.89970.90151.30.90320.90490.90660.90820.90990.91150.91310.91470.9162

39、0.91771.40.91920.92070.92220.92360.92510.92650.92790.92920.93060.93191.50.93320.93450.93570.93700.93820.93940.94060.94180.94290.94411.60.94520.94630.94740.94840.94950.95050.95150.95250.95350.95451.70.95540.95640.95730.95820.95910.95990.96080.96160.96250.96331.80.96410.96490.96560.96640.96710.96780.9

40、6860.96930.96990.97061.90.97130.97190.97260.97320.97380.97440.97500.97560.97610.976720.97720.97780.97830.97880.97930.97980.98030.98080.98120.98172.10.98210.98260.98300.98340.98380.98420.98460.98500.98540.98572.20.98610.98640.98680.98710.98750.98780.98810.98840.98870.98902.30.98930.98960.98980.99010.

41、99040.99060.99090.99110.99130.99162.40.99180.99200.99220.99250.99270.99290.99310.99320.99340.99362.50.99380.99400.99410.99430.99450.99460.99480.99490.99510.99522.60.99530.99550.99560.99570.99590.99600.99610.99620.99630.99642.70.99650.99660.99670.99680.99690.99700.99710.99720.99730.99742.80.99740.997

42、50.99760.99770.99770.99780.99790.99790.99800.99812.90.99810.99820.99820.99830.99840.99840.99850.99850.99860.998630.99870.99870.99870.99880.99880.99890.99890.99890.99900.9990Area under the curveZZa aArea under the curveWhereby,Shaded area = one-tailed probability of occurencea = 1 Shaded area硬商品买卖在阿里

43、巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Student t Distrbution exampleFDA requires pharmaceutical companies to perform extensive tests on all new drugs before they can be marketed to the public. The first phase of testing will be on animals, while the second phase will be on human on a limited basis. PWD i

44、s a pharmaceutical company currently in the second phase of testing on a new antibiotic project. The chemists are interested to know the effect of the new antibiotic on the human blood pressure, 和they are only allowed to test on 6 patients. The result of the increase in blood pressure of the 6 teste

45、d patients are as below: ( 1.7 , 3.0 , 0.8 , 3.4 , 2.7 , 2.1 )Construct a 95% confidence interval for the average increase in blood pressure for patients taking the new antibiotic, using both normal 和t distributions.硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Student t Distrbution example (cont)Using

46、normal or z distribution0.76 2.283 (0.388) 1.96 2.283 nS Z X interval confidence 95% S Deviation, Std 2.283 613.7 2.1) 2.7 3.4 0.8 3 (1.7 X Mean,0.05 level, Confidence6 n size, Sample2) / (aa95. 0 6Using student t distribution0.997 2.283 (0.388) 2.571 2.283 nS t X interval confidence 95% S Deviation

47、, Std 2.283 613.7 2.1) 2.7 3.4 0.8 3 (1.7 X Mean,1) - 6 (i.e. 5 freedom, of Degree0.05 level, Confidence6 n size, Sample2) / (aua95. 0 6Although the confidence level is the same, using t distribution will result in a larger interval value, because: 标准偏差标准偏差, S for small sample size is probably not a

48、ccurate 标准偏差标准偏差, S for small sample size is probably too optimistic Wider interval is therefore necessary to achieve the required confidence level 硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Summary of formula for confidence limitdata attribute for npq2) / Z( p limit Confidence data continuous for nS

49、2) / Z( X limit Confidenceknown is deviation standard population whenor ,30) (n size sample large Foraadata attribute for npq2) / t( p limit Confidence data continuous for nS2) / t( X limit Confidencedeviation standard population unknown with30) (n size sample small Foraa硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿

50、里巧巧软商品交易在阿里巧巧 6 Sigma 流程和流程和1.5 Sigma Shift in MeanStatistically, a 流程that is 6 Sigma with respect to its specifications is:Normal Distribution-6 -5 -4 -3 -2 -1 01 2 3 4 5 6 - 99.9999999998% -LSLUSLDPM = 0.002Cp = 2Cpk = 2But Motorola defines 6 Sigma with a scenario of 1.5 Sigma shift in meanDPM = 3

51、.4Cp = 2Cpk = 1.51.5 硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 某些某些Explanations on 1.5 Sigma Mean Shift Motorla has conducted a lot of experiments, 和found that in long term, the 流程mean will shift within 1.5 sigma if the 流程is under control.1.5 sigma mean shift in a 3 Sigma 流程control plan will be tran

52、slated to approximately 14% of the time a data point will be out of control, 和this is deem acceptable in statistical 流程control (SPC) practices.Normal Distribution-3 -2 -1 01 2 3 - 99.74% -LCLUCLDistribution with 1.5 Sigma Shift-3 -2 -1 01 2 3 - 86.64% -LCLUCLOut of control data points硬商品买卖在阿里巴巴硬商品买卖

53、在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Our Explanation Most frequently used sample size for SPC in industry is 3 to 5 units per sampling. Take the middle value of 4 as an average sample size used in the sampling. Assuming the 流程is of 6 sigma capability, is in control, 和is normally distributed. Under the confide

54、nce interval for sampling distribution, we expect the average value of the samples to fluctuate within 3 standard errors (i.e. natural tolerance), giving confidence interval of: data continuous for Sigma 1.5 X limit Confidence data continuous for 2Sigma 3 X limit Confidence data continuous for 4Sigm

55、a3 X limit Confidence data continuous for nSigma3 X limit Confidence硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Introduction to Hypothesis Testing ?What is hypothesis testing in statistic ? A hypothesis is “a tentative assumption made in order to draw out or test its logical or empirical consequences.

56、” A statistical hypothesis is a statement about the value of one of the characteristics for one or more populations. The purpose of the hypothesis is to establish a basis, so that one can gather evidence to either disprove the statement or accept it as true. lExample of statistical hypothesis The av

57、erage commute time using Highway 92 is shorter than using France Avenue. This 流程流程change will not cause any effect on the downstream 流程流程es. The variation of Vendor Bs parts are 40% wider than those of Vendor A.硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 Elements of Hypothesis TestingPossible outcomes

58、 for hypothesis testing on two tested populations:No Significant DifferenceSignificant Difference in VariationSignificant Difference in MeanSignificant Difference in both Mean 和和Variationm m1 m m2 1 = 2m m1 m m2 1 2m m1 = m m2 1 2m m1 = m m2 1 = 2硬商品买卖在阿里巴巴硬商品买卖在阿里巴巴 软商品交易在阿里巧巧软商品交易在阿里巧巧 为什么为什么Hypot

59、hesis Testing ? Many problems require a decision to accept or reject a statement about a parameter. That statement is a Hypothesis. It represents the translation of a practical question into a statistical question. Statistical testing 提供提供s an objective solution, with known risks, to questions which

60、 are traditionally answered subjectively. It is a stepping stone to 设计设计of Experiment, DOE.Hypothesis Testing Descriptions Hypothesis Testing answers the practical question: “Is there a real difference between A 和和B ?” In hypothesis testing, relatively small samples are used to answer questions abou