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Black Belt / Quality Engineering Statistics


Details Outline

Collecting and Summarizing Data

  1. Continuous vs. discrete data
  2. Measurement scales: nominal, ordinal, interval, and ratio
  3. Data collection methods: check sheets, coding data, and automatic gauging
  4. Effective sampling techniques: randomized, stratified, systematic, and representative
  5. Overview of measurement assurance and gauge R&R analysis
  6. Basic graphical tools: stem-and-leaf plots, box-and-whisker plots, run charts, scatter diagrams, frequency distributions, histograms, etc.

Basic Probability and Statistics

  1. Descriptive vs. inferential statistics
  2. Sample statistics vs. population parameters
  3. Basic probability concepts
  4. Measures of central tendency: mean, median, and mode
  5. Measures of dispersion: range, standard deviation, and variance

Properties & App. of Probability Distributions

  1. Effective use of the normal, binomial, Poisson, chi-square, student's t, and F distributions
  2. Overview of the hypergeometric, bivariate, exponential, lognormal, and Weibull distributions
  3. Testing distribution assumptions: normal probability plots, skewness and Kurtosis, chi-square goodness-of-fit tests
  4. Central limit theorem and sampling distribution of the mean

Confidence Intervals and Hypothesis Testing

  1. Statistical significance issues: statistical vs. practical significance, interpreting p-values, and type I and Type II (alpha and beta) errors
  2. Point and interval estimation: confidence intervals for means and proportions, prediction intervals, and tolerance intervals
  3. Hypothesis tests for population means, proportions, and variances
  4. Estimating sample sizes for confidence intervals and hypothesis tests
  5. Paired-comparison tests
  6. Contingency tables
  7. Nonparametric tests: Mood’s median, Levene’s test, Kruskal-Wallis, and Mann-Whitney.
  8. Analysis of Variance (ANOVA)

Exploratory Data Analysis

  1. Multi-vari charts: Distinguishing between positional, cyclical, and temporal variation
  2. Simple and multiple least-squares linear regression
  3. Simple linear correlation and correlation vs. causation
  4. Model diagnostics: evaluating model residuals

Course ID: 403

Course Duration: 32 hours

PDUs: 32 PMI

Course Formats


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