In this video, learn how to test your data sets for normality when conducting a Six Sigma project. This is essential because some tests require normal data, otherwise they will fail to work and give you the wrong conclusions.
- Let's say you want to conduct hypothesis testing…to prove differences in the population means…or variances between groups…or against a target value.…That output could be dimensions, processing times…or any characteristic that's continuous data.…Many statistical tests require…that continuous data follow a normal distribution,…commonly known as the bell-shaped curve.…But not all continuous data are normally distributed.…Testing for normality is essential,…because some tests require normal data.…
Otherwise they will fail to work,…and give you the wrong conclusions.…In another video, we look at a hypothesis testing road map…to show the correct test to use.…Let's look at the top left quadrant of that matrix.…For example, when comparing means,…the one sample t-test, the two sample t-test,…and ANOVA or analysis of variance…all assume the use of normal data.…When comparing variances, Bartlett's test…for equal variances also assumes normal data.…
If data is non-normal, then nonparametric tests…such as the one sample Wilcoxon test,…
Dr. Richard Chua builds upon his Six Sigma: Green Belt, Six Sigma Foundations, and Learning Minitab courses—which are prerequisites to this course—and covers an array of topics, including measurement system analysis, hypothesis testing, response surface methods, displaying improved process capabilities, and more.
- Process flow metrics
- Measurement system analysis
- Calculating process capability
- Hypothesis testing
- Confidence intervals
- Testing for normality
- Designing, conducting, and analyzing full-factorial experiments
- Using fractional factorial experiments for screening
- Displaying improved process capability
Skill Level Intermediate
1. Define Phase
Overview of the Define phase3m 51s
2. Measure Phase
3. Analyze Phase
4. Improve Phase
5. Control Phase
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