Get a solid foundation in statistical tools required for success as a Six Sigma Black Belt or Quality Engineer.
If you are planning to participate in Six Sigma Black Belt training, sit for the ASQ Six Sigma Black Belt exam or the Certified Quality Engineer exam you’ll get a refresher in statistical tools common to both exams’ Bodies of Knowledge.
- CEU Hours: 3.2
- Length: 32 Hours
- ASQ RU: 3.2
- Provider: ASQ - ILT
- Design and implement accurate and cost-effective data collection systems that will provide useful data for business process analysis.
- Graphically and mathematically summarize either small samples or large amounts of data in order to reach sound conclusions.
- Utilize the predictive power of probability distributions to project process performance in advance.
- Accurately estimate population characteristics from small sample groups.
- Evaluate sample data to determine if process interventions are truly effective or to compare various system options before making final decisions.
- Perform exploratory data analysis to detect process patterns and validate assumptions about process distributions patterns.
- Analyze data that does not fit into a particular probability distribution pattern.
- Develop mathematical models to predict business results from existing data.
- Determine the strength and direction of the relationship between two variables.
- Validate the effectiveness and completeness of predictive models.
- Communicate more effectively with others who require or use statistics in their business activities.
- Prevent the misuse and misinterpretation of business process data.
- Perform the quantitative analyses required for successfully completing Six Sigma Black Belt training and for the ASQ Certified Six Sigma Black Belt and Certified Quality Engineering exams.
Prerequisites:This is designed as an introductory course, so no prior knowledge of statistics is necessary, but a solid understanding of basic algebra and the ability to work basic algebraic formulas is required.
Who Should Attend:This is an excellent course for anyone looking to sit for the Six Sigma Black Belt or Quality Engineer exams, and is looking to gain a better understanding of the statistics requirements.
- Collecting and Summarizing Data
- Continuous vs. discrete data
- Measurement scales: nominal, ordinal, interval, and ratio
- Data collection methods: check sheets, coding data, and automatic gauging
- Effective sampling techniques: randomized, stratified, systematic, and representative
- Overview of measurement assurance and gauge R&R analysis
- Basic graphical tools: stem-and-leaf plots, box-and-whisker plots, run charts, scatter diagrams, frequency distributions, histograms, etc.
- Basic Probability and Statistics
- Descriptive vs. inferential statistics
- Sample statistics vs. population parameters
- Basic probability concepts
- Measures of central tendency: mean, median, and mode
- Measures of dispersion: range, standard deviation, and variance
- Properties and Applications of Probability Distributions
- Effective use of the normal, binomial, Poisson, chi-square, student's t, and F distributions
- Overview of the hypergeometric, bivariate, exponential, lognormal, and Weibull distributions
- Testing distribution assumptions: normal probability plots, skewness and Kurtosis, chi-square goodness-of-fit tests
- Central limit theorem and sampling distribution of the mean
- Confidence Intervals and Hypothesis Testing
- Statistical significance issues: statistical vs. practical significance, interpreting p-values, and type I and Type II (alpha and beta) errors
- Point and interval estimation: confidence intervals for means and proportions, prediction intervals, and tolerance intervals
- Hypothesis tests for population means, proportions, and variances
- Estimating sample sizes for confidence intervals and hypothesis tests
- Paired-comparison tests
- Contingency tables
- Nonparametric tests: Mood’s median, Levene’s test, Kruskal-Wallis, and Mann-Whitney.
- Analysis of Variance (ANOVA)
- Exploratory Data Analysis
- Multi-vari charts: Distinguishing between positional, cyclical, and temporal variation
- Simple and multiple least-squares linear regression
- Simple linear correlation and correlation vs. causation
- Model diagnostics: evaluating model residuals
This is a four-and-a-half day, instructor-led, face-to-face course.
The course includes a 300-page manual, an additional section of over 500 sample questions, and a T130X-Scientific Calculator.
- If you need to cancel, we will refund your paid registration fee as noted below.
- Requests for cancellations/transfers received at least 5 business days before the start of the course receive a full refund/transfer.
- Requests received within 5 business days of the course starting incur a $150 processing fee.
- After the course starts, there are no refunds or transfers.
- Registrants who fail to attend without advance notice are liable for the entire course fee.
- If you cannot find a substitute, we can transfer your course fees to another ASQ course of your choice.
- You must successfully complete the entire course or program before IACET CEUs and a Certificate of Completion can be awarded.
An instructor-led traditional classroom experience. Classroom-based instruction involves the highest level of instructor/student interaction. We offer classroom style training in two formats. See public and on-site.
ON-SITE TRAINING FOR YOUR ORGANIZATION.
ASQ’s customized on-site training programs are the cost-effective way for you to train employees on your schedule. Training on-site minimizes the cost-per-student, reduces travel expenses, and ensures that the content is specific to your exact needs.
5 Benefits of On-Site Training
- Expertise to your location: Saves time and travel expenses
- Convenient scheduling: Train groups of five or more
- Customizable courses: Training tailored to your groups’ needs
- Immediate results: Employees complete courses ready to apply what they’ve learned
- Value-add support: Instructors available to answer questions after session