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Program Description

Excel Orientation - Explore the Excel software package

Minitab Orientation - Explore the Minitab software package

Simulator Orientation - Explore the Process Simulator

Deterministic Reasoning - Describe a basic cause-and-effect relationship in terms of Y=f(X)

Leverage Principle - Relate the principle of leverage to an improvement project

Performance Yield - Explain why final yield is often higher than first-time yield

Hidden Processes - Describe the non-value added component of a process

Measurement Power - Describe the role of measurement in an improvement initiative

Establishing Baselines - Explain why performance baselines are essential to realizing improvement

Defect Opportunity - Understand the nature of a defect opportunity and its role in metrics reporting

Process Models - Define the key features of a Six Sigma performance model

Process Capability - Identify the primary indices of process capability

Design Complexity - Describe the impact of complexity on product and service quality

Variable Classifications - Define the various types of variables commonly encountered during quality improvement

Measurement Scales - Describe each of the four primary scales of measure and their relative power

Problem Definition - Characterize the nature of a sound problem statement

Focused Brainstorming - Explain how focused brainstorming is used to facilitate improvement efforts

Process Mapping - Understand how to define the flow of a process and map its operations

SIPOC Diagram - Describe the nature and purpose of an SIPOC diagram

Force-Field Analysis - Utilize force field analysis to solve problems

Matrix Analysis - Understand how matrices are created and used to facilitate problem solving

C&E Analysis - Explain how C&E matrices can be used to solve quality problems

Failure Mode Analysis - Understand how FMEA is used to realize process and design improvements

Performance Sampling - Explain how to design and implement a sampling plan

Check Sheets - Understand how check sheets can be used for purposes of data collection

Analytical Charts - Identify the general range of analytical charts that can be used to assess performance

Pareto Charts - Explain how Pareto charts can be used to isolate improvement leverage

Run Charts - Utilize run charts to assess and characterize time-based process data

Multi-Vari Charts - Define the major families of variation and how they can be graphed

Correlation Charts - Utilize a correlation chart to illustrate the association between two variables

Frequency Tables - Explain how to construct and interpret a frequency table

Performance Histograms - Construct and interpret a histogram and describe several purposes

Basic Probability - Understand basic probability theory and how it relates to process improvement

Pre-Control Charts - Describe the fundamental rules that guide the operation of a standard pre-control plan

Control Charts - Explain the purpose of statistical process control charts and the logic of their operation

Score Cards - Understand the purpose of Six Sigma score cards and how they are deployed

Search Patterns - Explain how the use of designed experiments can facilitate problem solving

Concept Integration - Understand how to sequence a given selection of quality tools to better solve problems

Quality Simulation - Employ the related quality tools to analyze data generated by the process simulator

Performance Variables - Identify and describe the types of variables typically encountered in field work

Statistical Notation - Recognize and interpret the conventional forms of statistical notation

Performance Variation - Explain the basic nature of variation and how it can adversely impact quality

Normal Distribution - Describe the features and properties that are characteristic of a normal distribution

Distribution Analysis - Explain how to test the assumption that a set of data is normally distributed

Location Indices - Identify, compute, and interpret the mean, median, and mode

Dispersion Indices - Identify, compute, and interpret the range, variance, and standard deviation

Quadratic Deviations - Understand the nature of a quadratic deviation and its basic purpose

Variation Coefficient - Compute and interpret the coefficient of variation

Deviation Freedom - Explain the concept of degrees-of-freedom and how it is used in statistical work

Standard Transform - Describe how to transform a set of raw data into standard normal deviates

Standard Z-Probability - Describe how to convert a standard normal deviate into its corresponding probability

Central Limit - Understand that the distribution of sampling averages follows a normal distribution

Standard Error - Recognize that the dispersion of sampling averages is described by the standard error

Student's Distribution - Understand that the T distribution applies when sampling is less than infinite

Standard T-Probability - Describe how to convert a T value into its corresponding probability

Statistics Simulation - Employ basic statistics to analyze data generated by the process simulator

Statistical Inferences - Explain the concept of a statistical inference and its primary benefits

Statistical Questions - Explain the nature and purpose of a statistical question

Statistical Problems - Understand why practical problems must be translated into statistical problems

Null Hypotheses - Define the nature and role of null hypotheses when making process improvements

Alternate Hypotheses - Define the nature and role of alternate hypotheses when making process improvements

Statistical Significance - Explain the concept of statistical significance versus practical significance

Alpha Risk - Explain the concept of alpha risk in terms of the alternate hypothesis

Beta Risk - Define the meaning of beta risk and how it relates to test sensitivity

Criterion Differences - Explain the role of a criterion difference when testing hypotheses

Decision Scenarios - Develop a scenario that exemplifies the use of hypothesis testing

Sample Size - Define the statistical elements that must be considered when computing sample size

Mean Distribution - Comprehend and characterize the distribution of sampling averages

Mean Interval - Compute and interpret the confidence interval of a mean

Variance Distribution - Comprehend and characterize the distribution of sampling variances

Variance Interval - Compute and interpret the confidence interval of a variance

Proportion Distribution - Comprehend and characterize the distribution of sampling proportions

Proportion Interval - Compute and interpret the confidence interval of a proportion

Frequency Interval - Describe how frequency of defects is related to confidence intervals

Mean Differences - Determine if two means are statistically different from each other

Variance Differences - Determine if two variances are statistically different from each other

Variation Total - Compute and interpret the total sums-of-squares

Variation Within - Compute and interpret the within-group sums-of-squares

Variation Between - Compute and interpret the between-group sums-of-squares

Variation Analysis - Explain how the analysis of variances can reveal mean differences

One-Way ANOVA - Construct and interpret a one-way analysis-of-variance table

Two-Way ANOVA - Construct and interpret a two-way analysis-of-variance table

N-Way ANOVA - Construct and interpret an N-way analysis-of-variance table

ANOVA Graphs - Construct and interpret a main effects plot as well as an interaction plot

Linear Regression - Conduct a linear regression and construct an appropriate model

Multiple Regression - Conduct a multiple regression and construct an appropriate model

Residual Analysis - Compute and analyze the residuals resulting from a simple regression

Parametric Simulation - Apply general regression methods to the process simulator

Statistical Definition - Describe how to translate a practical problem into a statistical problem

Model Fitting - Explain what is meant by the term "Model Fitting" and discuss its practical role in Six Sigma work

Testing Independence - Explain how a test of independence can be related to the idea of correlation

Contingency Coefficients - Understand how a contingency coefficient relates to a cross-tabulation table

Yates Correction - Describe the role of Yates correction in terms of the chi-square statistic

Testing Proportions - Test the significance of two proportions using the Chi-square statistic

Research Design - Explain how the idea of research design fit with the idea of problem Solving

Information Sources - Explain how the idea of research design fit with the idea of problem Solving

Questionnaire Construction - Describe the role of survey demographics when analyzing closed-form survey data

Formulating Questions - Identify several things that should be avoided when developing survey questions

Question Quality - Explain what is meant by the term "question quality" and how this idea relates to data analysis

Sampling Plans - Describe several different types of sampling plans commonly used in survey research

Data Analysis - Explain how categorical survey data can be analyzed to establish strength of association

Nonparametric Concepts - Explain the difference between parametric and nonparametric methods

Median Test - Execute a median test on two groups and then determine if the difference is statistically significant

Runs Test - Conduct a runs test to determine if a time series pattern is random

Other Tests - Identify two nonparametric methods other than a median or runs test

Measurement Uncertainty - Understand the concept of measurement uncertainty

Measurement Components - Describe the components of measurement error and their consequential impact

Measurement Studies - Explain how a measurement systems analysis is designed and conducted