This webinar explains what it means to be “normally distributed”, how to assess normality, how to test for normality, and how to transform non-normal data into normal data.
Attend this webinar to understand the probability plot is a powerful graphical tool that assists in diagnosing whether the data is 1) normal, 2) skewed or 3) contains multiple populations. A multitude of examples will be provided to diagnosis various data patterns that probability plots display. Guidelines on identifying candidate distributions will also be provided with respect to historical applications, range and skewness of the data. Statistical tests for goodness of fit will also be discussed as a supplement to the probability plot. Graphical output from Minitab® will be used.
Why Should You Attend:
Have you ever exclaimed, “OH NO, my data is NOT NORMAL”? Fitting the appropriate distribution to your data is critical to accurately estimating product reliability, capability of a process, or meeting distributional assumptions for statistical analysis. Failure to identify the appropriate data distribution could result in false acceptance or product claims.Attend this webinar to understand the probability plot is a powerful graphical tool that assists in diagnosing whether the data is 1) normal, 2) skewed or 3) contains multiple populations. A multitude of examples will be provided to diagnosis various data patterns that probability plots display. Guidelines on identifying candidate distributions will also be provided with respect to historical applications, range and skewness of the data. Statistical tests for goodness of fit will also be discussed as a supplement to the probability plot. Graphical output from Minitab® will be used.
Learning Objectives:
- Interpret probability plots for symmetry, skewness and multiple populations
- When to use appropriate Statistical Test for Distribution Fitting
- Identify candidate distributions and determine best fitting distribution to estimate product reliability or process percent defective.
Areas Covered in the Webinar:
- Graphical Tools used to verify distribution assumptions (Histograms, Box-plots, Probability Plots)
- Interpreting probability plots (e.g. Normal, skewed, bi-modal)
- Interpreting Statistical tests for normality (Anderson-Darling, Ryan-Joiner, Kolmogorov-Smirnov)
- Probability distributions (e.g. Normal, Lognormal, 3-Parameter Weibull)
- Normalizing Transformations (e.g. Box-Cox, Lognormal, Johnson)
Who Will Benefit:
- Quality professionals
- Regulatory professionals
- Compliance professionals
- Manufacturing engineers
- Production engineers
- Design engineers and managers
- Quality engineers
Course Provider
Jerry Phillips,
