Overview

Welcome to the Tabtrainer® Essentials Series - your foundation for mastering statistical analysis in engineering and quality control.In this hands-on course, you will learn how to analyze real industrial data using correlation and regression methods in Minitab®. From pairwise relationships and Pearson coefficients to fitted line plots and prediction intervals, this training gives you the tools to detect patterns, model relationships, and support data-driven decisions.You'll explore simple regression, multiple correlation, and matrix plots, and understand how to validate your insights with p-values and confidence intervals.Led by Prof. Dr. Murat Mola, TÜV-certified Six Sigma trainer and Professor of the Year 2023 in Germany, this course bridges theory and application for engineers, analysts, and quality professionals who want to base their decisions on solid statistical evidence.By the end of this training unit, participants will: Correlation AnalysisUnderstand the concept of correlation as a measure of the linear relationship between two continuously scaled variables.Learn to perform a Pearson correlation analysis using Minitab, selecting appropriate variables and interpreting the correlation coefficient (r).Interpret correlation values qualitatively (positive, negative, none) and quantitatively (strength and direction).Understand the role of the p-value and 95% confidence interval in evaluating whether a correlation is statistically significant.Apply correlation analysis to real process data and recognize its limitations in predicting future values.Regression AnalysisGrasp the difference between correlation and regression: while correlation assesses the relationship, regression predicts unknown values and models cause-and-effect.Learn the method of least squares to derive a regression function manually and with software.Use Minitab to generate a fitted line plot, determine the regression equation, and interpret model quality using R-squared, adjusted R-squared, and ANOVA p-values.Apply the regression model to predict copper content required for a target material strength (280 MPa), even in data-free ranges.Understand and differentiate between:Confidence intervals (range for the population mean)Prediction intervals (range for a future individual value)Make informed, data-based recommendations for process control, supported by statistically validated predictions.Multiple Correlation Analysis / MatrixplotUnderstand the purpose and application of multiple correlation analysis in a real-world industrial context.Use a matrix plot to visually identify strong pairwise correlations among multiple quantitative variables.Interpret the Pearson correlation coefficient to assess the degree of linear relationship between variables.Perform and interpret the pairwise Pearson correlation test, including significance evaluation via p-values.Derive data-driven recommendations based on statistical evidence-such as identifying redundancies among test pilots.Recognize the importance of statistical significance in distinguishing between assumed and actual correlations in a population.Save and document correlation analysis results as part of a structured data analysis project.

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