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Introduction to MINITAB and Basic Data Analysis In this course Exsilon will showcase the principles of statistical process control (SPC) and capability analysis from basic tools to more advanced techniques, not currently taught in the standard quality improvement programs. You will see how to select the right control chart and capability analysis for specific challenges, avoid the common mistakes many practitioners make, and model processes that you previously thought too complex. You will learn how to incorporate statistical thinking and problem solving into your process improvement initiatives by using simple graphical and numerical techniques to evaluate and characterize process performance and discover causes for process variability. And because Exsilon teaches without bias, we will highlight the strengths and weaknesses of MINITAB to illustrate how best to use the application In addition you will:
Statistical Analysis for Process Improvement using MINITAB This course builds upon the prerequisite Introduction to MINITAB and Basic Data Analysis. Statistical analysis of data has become an integral part of almost all quality improvement efforts and practitioners are required to extract valuable information from their processes. This course illustrates statistical tools and techniques that teach how to objectively evaluate processes including ways to quantify and incorporate the uncertainty that accompanies all statistical analyses. This is addressed using formal statistical analysis techniques to estimate critical process characteristics and statistically test theories your team has about those processes. In addition you will:
Design and Analysis of Experiments using MINITAB This course builds upon the prerequisites Introduction to MINITAB and Basic Data Analysis and Statistical Analysis for Process Improvement using MINITAB. Design and Analysis of Experiments is one of the most powerful sets of statistical tools critical to process optimization. This course will dispense with the simplified examples typically taught in most training courses—that rarely occur in practice—and will address the issues necessary for real world experimentation. Your company will learn how to design and analyze experiments that not only evaluate the integral factors under study but also address the many uncontrollable and unknown external factors that impact all experiments. By honing these skills your team will have the ability to expedite quality improvement efforts by successfully predicting and modeling how processes perform even within complex environments. In addition you will:
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