In this module, you will learn how to measure and model relationships between variables. You will explore the correlation coefficient, linear regression, and multivariate tools. The module also delves into applying the key concepts of hypothesis testing, such as the significance of testing, calculating sample size, and analyzing waste. You will become acquainted with techniques such as point and interval estimates and tests for means, variances, and proportions. Additionally, you will learn the analysis of variance (ANOVA) and goodness-of-fit (chi-square) tests and the techniques for analyzing and managing risk.

In this module, you will explore the key concepts of the design of experiments (DOE). You will also learn how to apply the principles of DOE, such as power, sample size, balance, repetition, replication, order, efficiency, randomization, blocking, interaction, confounding, and resolution. The module will take you through planning and evaluating different types of experiments in DOE and various types of Lean methods you can use for process improvement, like waste elimination, cycle-time reduction, Kaizen, and others. Additionally, the module focuses on statistical process control (SPC) and other controls that help to streamline business processes. Finally, you will learn how to sustain process improvements using methods like documentation, training, and evaluation.

This is a peer-review assignment based on the concepts taught in the Advanced Statistical Analysis and Tools course. In this assignment, you have been provided with a real-life scenario. You must explain how you can use process capabilities and their related metrics in process improvement.