Six Sigma Green Belt: Measure

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Modeling and Analyzing Processes in Six Sigma
Credit NASBA: 2.5

Statistics and Probability in Six Sigma
Credit NASBA: 2

Data Classification and Collection in Six Sigma
Credit NASBA: 2

Summarizing and Presenting Data in Six Sigma
Credit NASBA: 2

Probability Distributions and Measurement Systems Analysis in Six Sigma
Credit NASBA: 2.5

Measuring Process Capability and Performance in Six Sigma
Credit NASBA: 1.5

Structura cursului

Modeling and Analyzing Processes in Six Sigma

  • identify basic concepts associated with process modeling

 

  • match the process map types to the situations for which they best apply

 

  • identify correct interpretations of a process map, in a given scenario

 

  • match key process map symbols with their use

 

  • identify the key characteristics of written procedures and work instructions

 

  • recognize various SIPOC elements in a given organizational process

 

  • recognize the use of best practice when creating a cause-and-effect diagram

 

  • prioritize process input variables in a Six Sigma improvement project using relational matrices

 

 

 

Statistics and Probability in Six Sigma

  • classify examples of enumerative and analytical statistics

 

  • distinguish between examples of population parameters and sample statistics

 

  • define the statistical terms "population" and "sample"

 

  • identify key concepts of the Central Limit Theorem

 

  • calculate the probability of an event in a given scenario

 

  • match the terms associated with probability to their definitions

 

  • calculate the probability of an independent event in a given scenario

 

  • calculate the probability of a dependent event in a given scenario

 

  • calculate the probability of a mutually exclusive event in a given scenario

 

  • identify how to use the multiplication rule to calculate probabilities of events

 

  • identify how to use the addition rule to calculate probabilities of events

 

 

 

Data Classification and Collection in Six Sigma

  • distinguish between examples of continuous and discrete data

 

  • identify the key characteristics of continuous and discrete data

 

  • match the types of measurement scales to their descriptions

 

  • match the key considerations for creating a solid data collection plan to their examples

 

  • identify an example of a well-created check sheet

 

  • identify examples of different types of data coding methods

 

  • match the different types of sampling methods used in Six Sigma to their descriptions

 

  • identify the considerations for determining sample size

 

  • identify the key characteristics of simple random sampling

 

  • identify the tasks associated with taking a stratified sample

 

 

 

Summarizing and Presenting Data in Six Sigma

  • calculate the key measures of central tendency

 

  • calculate the key measures of dispersion

 

  • make key calculations in the creation of a cumulative frequency table

 

  • sequence the examples of steps for constructing a frequency distribution table

 

  • distinguish between the characteristics of stem-and-leaf plots, box-and-whisker plots, and Pareto charts

 

  • match run chart pattern names to their appropriate interpretations

 

  • identify statements that reflect correct interpretations of a scatter diagram

 

  • identify the general interpretations associated with the three types of histograms

 

  • sequence the examples of steps for creating a normal probability plot

 

 

 

Probability Distributions and Measurement Systems Analysis in Six Sigma

  • identify correct observations of a normal distribution curve

 

  • use a z-distribution table to calculate the cumulative probability of the z-value

 

  • calculate probability using binomial distributions

 

  • calculate probability using the Poisson distribution formula

 

  • calculate the cumulative probability of chi-square using a cumulative frequency table

 

  • calculate the chi-square statistic

 

  • calculate the t-statistic for a given data set

 

  • calculate the F-statistic for a given data set

 

  • match the key measurement systems analysis concepts to their characteristics

 

  • calculate the Gauge Repeatability and Reproducibility (R&R) value

 

  • match measurement-correlation factors to their definitions

 

 

 

Measuring Process Capability and Performance in Six Sigma

  • identify key concepts associated with process capability and stability

 

  • identify the correct interpretation of a given process capability diagram

 

  • distinguish between the two methodologies used to measure process performance

 

  • match the essential stages of a process capability study to their descriptions

 

  • calculate and interpret process capability indices for a given data set

 

  • calculate process performance indices

 

  • distinguish between the characteristics of process performance indices and process capability indices

 

  • approximate long-term process capability using the sigma level for short-term process capability

 

  • calculate process capability for a given discrete data set

 

 

 

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