Six Sigma Glossary

Six Sigma glossary is a collection of terms and definitions commonly used in Six Sigma. Most of the Six Sigma courses include the glossary in their courseware.

The Six Sigma glossary is a collection of terms which are commonly used in Six Sigma and it is quite essential for the learners to know the definitions of all the terms in detail. Here are some of the important terms from the Six Sigma glossary:

    • Analysis of Variance: A statistical tool used to determine if the samples from populations have the same mean or have variant significant mean.
    • Analytical Statistics: Inferential statistical tools that are used to draw conclusions about a population from a sample of data.
    • Bayes Theorem: Used in probability theory to estimate the probability of an outcome.
    • Bimodal Distribution: Continuous distribution that has two peaks. It occurs when two processes are mixed.
    • Binomial Distribution: It is a kind of discrete probability distribution, showing the probability of getting number of successes in a sample of data from an infinite population where the probability of success is already assumed.
    • Cause and Effect Diagram: Also known as fishbone diagram, it is used to list the causes of a problem in separate categories.
    • Common Cause Variation: This is variation from the normal distribution, caused by a number of factors that invariably affect any project.
  • Control Charts: These are diagrams that indicate and monitor the output of the process.
    • DPMO: It stands for Defects Per Million Opportunities. It states the opportunity where a defect may take place.
  • DFMEA: It stands for Design Failure Mode and Effect Analysis, which determines the possibility of the failure of a design and its consequences.
  • Exponential Distribution: It is a continuous probability distribution, which determines the function of an item until affected by external factors.
  • Factor Analysis: It is a kind of multivariate analysis. It takes a number of continuous factors and integrates and converts them into a lesser number of factors, which affect the process.
  • Frequency Distribution: It is a diagram showing the frequency of observations in a certain range of values.
  • Gantt Chart: It shows the breakdown of work against time.
  • Gap Analysis: It is a method to analyze the gap between the achieved performance and potential performance.
  • Hyper Geometric Distribution: It is a kind of discrete probability distribution that takes a sample from a small population without any replacement.
  • Hypothesis Testing: It is a method that determines the probability of two propositions.
  • ISO9000:2000: This is an international standard for quality management. It documents mainly three aspects, fundamentals and vocabulary, requirements and guidelines for performance improvements.
  • Interaction: All factors interact with each other to produce a separate outcome. This is an important aspect for experimental design.
  • Juran Joseph: He is one of the quality leaders who proposed the Quality trilogy that includes planning, control and improvement.
  • Kaizen: It is a method used to control and maintain the improvement in the lean production.
  • Lean Production: It is a method of production where the focus is on the elimination of waste.
  • Lognormal Distribution: A type of continuous probability distribution with probability distribution function, used mainly in modeling of material properties.
  • Mann–Whitney Test: It is nonparametric version of the two sample t-tests, comparing the means of the two. It is a kind of hypothesis test as well.
  • Multi Variant Analysis: This deals with processes that have many or multi inputs and/or outputs.
  • Normal Distribution: It is a continuous probability distribution with no special factors affecting it.
  • NPV: This stands for Net Present Value that helps in determining the financial benefits of long-term projects in a company.
  • Ordinal Scale: A measurement with a natural ordering of levels but there is no definition of the categories.
  • Pareto Principle: It works on the 20:80 principle like 20% of the population owns 80% of the resources, 20% of the causes will result to 80% of the problems or such.
  • P-value: It is the probability of the observed outcomes if the null hypothesis is true.
  • Quality Function Deployment: It is a method used to relate the customers’ needs and incorporates the specific characteristics and utility of the products.
  • Regression Analysis: It uses the least square method to find the line of best fit through a point series.
  • Root Cause Analysis: It works on the concept that there is a hidden cause that gives rise to the entire direct problem and tries to resolve it.
  • Six Sigma: It is the management strategy that aims at achieving a quality target of 3.4 DPMO for its products.
  • SWOT Analysis: A strategic kind of analysis that stands for Strengths, Weaknesses, Opportunities and Threats of a process.
  • Temporal Variation: It is a variation that depends on time. It may also be cyclical.
  • Theory of Constraints: It follows the concept that any organization has one or two bottlenecks and improving just any workstation, and not to the bottlenecks, doesn’t really bring about any result.
  • Unimodal Distribution: A distribution that has a single locally placed maximum
  • Variation: It is the measurement of the dispersion of the data set.
  • Weibull Distribution: A very flexible continuous probability distribution, with the option of cutting and modifying the product characteristics, used in reliability engineering purpose.
  • XmR Charts: ImR charts, or Individual and Moving Range charts, mainly used when the subgroup is one.
  • Yield: Related to the rolled throughout yield is a Six Sigma term that works on the probability of a unit passing through a process without any defects. It is first yield through the first step.
  • Z-Test: It is a hypothesis that tests the mean to a specific value. It is mainly used when the standard deviation is known or the sample is large.

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