六西格瑪專業(yè)述語的英文解釋.ppt

1、Glossary,4-Block a error a risk Accuracy Active (opportunity or defect) Advocacy Team Alternate Hypothesis ANOVA ANOVA method (Gauge R reduces correlation between Xs.A fundamental statistical theorem stating that the distribution of averages of a characteristic tends to be normal, even when the pare

2、nt population is highly non-normal.,Central Composite Design Champion Champion Review Chi-Squared test Classical Yield Common Cause Variation Components Search Confidence Confidence Interval Consumer Continuous Data ,A Design of Experiments (DoE) method where each X is tested at 5 levels (see Star P

3、oints). A CCD provides the capability to model aprocess with a quadratic equation OR a linearequation.Typically a director - someone who can support the Six Sigma project and has the authority to remove barriers and provide resources. Takes an active part in Project Review.A regular meeting to prese

4、nt Six Sigma projects, share experiences and remove roadblocks.Hypothesis test for discrete data. Evaluates the probability that counts in different cells are dependent on one another, or tests Goodness of Fit to some a priori probability distribution.See “First Pass Yield”. Good units produced divi

5、ded by Total Units Produced.See “White Noise”. The inherent variation of a process, free from external influences. Usually measured over a short time period. A method of screening for Vital Few Xs in manufactured assemblies. Also known as Part Swapping. The complement of alpha risk. Confidence = 1-a

6、.A range of plausible values for a population parameter, such as mean or standard deviation.The end user of a product (the homeowner, for e.g.). The consumer is external to the business.Data that can be meaningfully broken down into smaller and smaller increments - e.g. length, temperature etc.),Con

7、tour Plot Control Limits Cost of Quality Cp Cpk CQ CTQ Cube Plot Customer Data Window Defect Dependent Variable ,A graph used to analyze experiments of a Central Composite Design. Two Xs comprise the axes, and levels of constant Y are shown in the body of graph. Resembles a topographical map. Lines

8、on a Statistical Process Control (SPC) chart that represent decision criteria for taking action on the process. Lines are drawn +/- 3 standard deviations (s) from the mean. A financial reconciliation of all the costs associated with defects (scrap, rework, concessions etc.) Statistic used to measure

9、 Process Capability. Assumes data is centred on target. Similar in concept to Z.stStatistic used to measure Process Performance. Does not assume centred data. Similar in concept to Z.ltCommercial Quality. Used to categorize non-manufacturing projects that impact the consumer and/or customer.Critical

10、-to-Quality characteristic. An aspect of the product or service that is important to the customer/consumer.A graph used for analysis of the results of a factorial designed experiment (DoE). Shows test conditions that optimize the response.The recipient of the output of a process. May be internal (e.

11、g. Assembly is a customer of finishing shops), or external (e.g. Currys, Belling etc.) who then sell our products to consumers.The spreadsheet window in Minitab where data is entered for analysis.Any aspect of a part or process that does not conform to requirements.The output of a process. The “Y” r

12、esponse.,Descriptive Statistics Design of Experiments (DoE) Discrete Data Dotplot DPMO DPO DPU e (Exponential Function) Entitlement Executive Summary F-test ,Mean, Standard Deviation, Variance and other values calculated from sample characteristics. Also includes assorted graphs.A statistical field

13、of study where independent variables (Xs) are systematically manipulated and the response observed. Used to demonstrate which Xs are the Vital Few, and to optimize the response.Data that can only be described by levels, i.e. pass/fail, operator a/b/c, integer values (e.g. number of defects). Data th

14、at cannot be broken down into finer increments.Frequency diagram representing data by dots along a horizontal axis. Generally used as an alternative to a histogram for small sample sizes.Defects Per Million Opportunities - 1,000,000 multiplied by total number of defects, divided by the total number

15、of opportunities. A metric for defects equivalent to ppm used for defectives.Defects Per Opportunity - total number of defects divided by total number of opportunities. Used to enter the Normal Table to obtain Z values.Defects per unit - total number of defects divided by total number of units. Used

16、 primarily to calculate Rolled Throughput Yield (Y.rt) through the Poisson formula Y.rt = e-DPU. A mathematic constant roughly equal to 2.718Mathematical identity: ln(e)=1 Z.st The best the process can be. What the process would look like if all Assignable Cause Variation was controlled. The first p

17、age of output from the Minitab Process Capability selection. A test to compare variances of 2 or more samples, and to compare the equality of two or more means (in ANOVA).,Factorial Experiment Fractional Factorial Experiment. First Pass Yield FMEA Functional Owner GaugeXBR method Gantt Chart Gauge R

18、 the lower graph (Moving Range) plots the difference between sequential data as points on the chart. Control limits are also calculated for this chart. Variables (Xs) that influence the response of a dependent variable (Y) Statistical analyses that quantify the risk of statements about populations,

19、based on sample data. Inferential statistics are usually hypothesis tests or confidence intervals.The Best the process can be, with only variation due to white noise present. See Entitlement, Z.st A graph used to analyse factorial and fractional factorial designs of experiments. Indicates the effect

20、 on Y when two Xs are changed simultaneously. The greater the difference in slopes between the Xs, the greater the interaction.,Kurtosis L1 Spreadsheet L2 Spreadsheet LCL (Lower Control Limit) Leverage Variable Linearity (gauge) Long term data LSL m Macro Main Effects Plot Master Black Belt ,Compari

21、son of the height of the peak of a distribution to the spread of the tails. The kurtosis value is 3 for a perfect normal distribution. Excel spreadsheet for discrete data that calculates subsystem Z values and rolls them into a system-level Z value. Replaced by Product Report in Minitab release 11.2

22、Excel spreadsheet for continuous data that calculates Z.st and Z.ltReplaced by Process Reports in Minitab release 11.2The lower control boundary on a Statistical Process Control (SPC) chart. A limit calculated as the mean minus 3 standard deviations. Note: SEM (Standard Error of the Mean) is used fo

23、r s; stdev = s/sqrt(n).An X variable with a strong influence on the Y response. One of the Vital Few.The difference in the accuracy of the gauge from the low end to the high end of the test range.Data obtained in such a way that it contains assignable cause variation (black noise).Lower Specificatio

24、n LimitThe mean or average of a population A mini program within a software package designed to provide a particular output (e.g. Gauge R skewness is negative if the distribution is shifted to the right, positive if shifted to the left. The requirements of a design, usually expressed as a target (or

25、 nominal) value with an associated allowable tolerance for variation (e.g. 5.00cm +/- 0.05 cm) How far the data is distributed away from their mean. Consistency of measurement values obtained with the same gauge on the same set of parts, with measurements taken at different times. Gauge instability

26、can lead to calibration issues. A statistical measure of spread or dispersion from a mean value.,Standard Error of the Mean Standard Normal Deviate Standard Order Star Point(s) Statistical Problem Statistical Process Control Statistical Solution Statistics Stepwise Regression Structure Tree ,The sta

27、ndard deviation of xbar, based on a sample size of n. (Also a correction factor for standard deviation of relatively small sample sizes (30).) Reduces the standard deviation of the sample by sqrt(n). SEM = s/sqrt(n). See “Z transform”. A feature of factorial Design of Experiments (DoE) that determin

28、es the order of the high/low settings of the Xs for each run of an experiment by using a pre-determined pattern of +1s and -1s for each X. Extreme test points in a Central Composite Design of Experiments. Found by taking the fourth root of the number of Cube points (factorial points) in the design a

29、nd adding/subtracting this value from the Centre Point. The outcome of the Analyze phase. Is the problem centring, spread or both? SPC. A graphical method of monitoring a process and determining statistically when the process requires attention by comparing it to a historical mean and calculated con

30、trol limits at +/- 3 sigma. Output of the Improve phase. Where do the Xs need to be set to control the Y? The study of variation, including methods of describing, quantifying and reducing variation, as well as estimating risks. A regression technique where the model is developed one step at a time,

31、adding X variables one at a time to the model in order of their contribution to changes in Y. A problem solving tool listing the characteristics of interest on one side of the page, and showing contributing factors to the characteristics as branches.,Subgroup Sustained Process Capability t-test Targ

32、et Technical Requirement Test Sensitivity (d/s) Tolerance TOP (Total Opportunities) Transfer Transform Trivial Many Xs UCL (Upper Control Limit) Unit ,A sample of like parts or related data taken consecutively that contains only inherent process variation (white noise) Capability of a process in the

33、 long term Z.lt A statistical test used to compare two means, or to compare a mean to a standard value. The specified or desired average of a process Physical or process characteristic that must be controlled to address a Consumer Cue - also known as “The Gap”. A statistic used to determine sample s

34、ize for hypothesis testing. Compares the difference in means to the spread of the data. The amount of variation allowable by design in a process. Tolerance = USL-LSL. Number of opportunities per unit times the number of units. The last phase of a Six Sigma project, where knowledge gained is transfer

35、red to all other similar processes - ie synergy. Any mathematical relationship used to translate data of one space into data of another space (e.g. transforms to convert non-normal data to normal data; log, reciprocal, power functions etc.) The 80% of the independent variables (Xs) that generate onl

36、y 20% of the total process variation. Variables that influence the process, but at a much less significant level than the Vital Few. The upper control boundary on a Statistical Process Control (SPC) chart. A limit calculated as the mean plus 3 standard deviations. NOTE: SEM (Standard Error of the Me

37、an) is used for s: stdev = s/sqrt(n) A user-defined quantity representing the output of a process. May be a part, system,Unit USL Variance Vital Few Xs White Noise X X-bar X-bar/R chart Y response Y.ft Y.na Y.rt Z.bench ,A user-defined quantity representing the output of a process. May be a part, sy

38、stem, component of a part or a sub-system. Upper Specification Limit (Standard Deviation)2 The 20% of the independent variables that generate 80% of the total process variation. These are Xs which must be controlled to bring a process to Six Sigma levels of performance. See Common Cause Variation. The natural variation within the process, free of external influences. The independent variable(s), or input(s), of a process. The mean or average of a sample. The sum of all data in the sample divided by th