Statistical Process Control and Capability Analysis with R

In any quality improvement initiative, making decisions based on unstable processes leads to costly errors and false assumptions. Understanding whether a process is in statistical control is the critical first step before conducting hypothesis testing or making strategic changes. This text-based course guides you through the foundational concepts of Statistical Process Control (SPC) and process capability. You will learn how to read, interpret, and write R code to analyze both continuous and discrete data, construct control charts, and determine if your processes can consistently meet specification limits. What you'll learn: - Understand the fundamental principles of statistical control, process stability, and why stability must precede hypothesis testing. - Analyze continuous and discrete quality data using modern R libraries and tidy data principles. - Create and interpret key statistical process control charts, including variable and attribute charts. - Apply probability-based control rules to detect out-of-control patterns and process shifts. - Calculate and interpret process capability indices to evaluate performance against specifications. - Practice basic data cleaning and prepare quality metrics reports using reproducible R workflows. You will start with core quality improvement terminology and statistical foundations before progressing to step-by-step R code walkthroughs and practical data analysis exercises. The curriculum flows logically from establishing control to measuring capability, ensuring you build a highly functional toolkit. This course is designed for beginners in quality engineering, operations, or data analysis, with no prior experience in R or advanced statistics required. Start reading today to master the essential tools of statistical quality control and make confident, data-driven process improvements.