One of the more advanced tools in Six Sigma is Analysis of Variance (ANOVA). It can be used to breakdown variation in a process from both categorical or variable inputs of the process. By comparing your output data to your inputs, you can draw a statistical relationship (or mathematical equation) that explains how the inputs are correlated to the outputs.
Regression and ANOVA are useful for analyzing existing data from the process, to determine which variables (or factors) are most important.
In the class, we will go through a simple explanation of Regression, and breakdown the ANOVA table to explain how each column in calculated.
We will also talk about p values, that help you determine which factors are statistically significant. We will also explain how to analyze the residuals of an analysis, and understand the R-squared values, to make sure your results are valid. After analysis is complete, you will have an equation for how your inputs and outputs correlate to each other.
We will use Minitab statistical software to provide example data sets from various industries.
Upon completion of the workshop, you will be able to analyze your data using Regression and ANOVA, evaluate the strength of the model, and determine which factors are statistically significant.