Data Collection and Root Cause Analysis

Root Cause Analysis Techniques

This module explains the differences between value-added and non-value-added activities. It makes a case for non-value-added activities that are necessary to enable the smooth running of the organization. The module also discusses how to identify the bottlenecks in a system and suggests ways to eliminate them. Lastly, the module explores the various techniques to conduct a root cause analysis (RCA) for the identified problem in your process or organization. The first one, Pareto analysis, is based on the Pareto principle, which states that approximately 80% of the effects come from 20% of the causes. This analysis helps prioritize potential root causes based on their relative impact. You will also learn how to use the fishbone diagram, also known as the Ishikawa or cause and effect diagram, which visually represents the potential causes contributing to a problem while categorizing the possible causes into specific groups to facilitate the identification of root causes. Additionally, you will learn about the five whys, a simple yet powerful technique involving repeatedly asking “why” to identify the root cause of a problem. It helps to peel the layers of symptoms and surface-level causes to get to the core issue.

Hypothesis Testing and Investigating the Relationship

This module provides a comprehensive overview of hypothesis testing, an essential statistical tool used to assess the validity of claims or hypotheses about populations. You will learn about the hypothesis testing process, its application in real-world scenarios, and how to interpret the hypothesis test results to make better decisions. The module will take you through the different types of hypotheses, types of errors, and the significance of the p-value in hypothesis testing. You will also learn about the principles and applications of correlation and regression techniques. The module discusses the types of correlation and the roles of dependent and independent variables in regression analysis. lt explains the implications of R-squared values in regression analysis. The module also explains simple linear regression and the difference between deterministic and probabilistic models.

Peer-Reviewed Assignment

This is a peer-review assignment based on the concepts taught in the Data Collection and Root Cause Analysis course. In this assignment, you will apply your knowledge of hypothesis testing to a real-life scenario.