Process Capability Analysis for Non-Normal Data Using Excel

Practical Process Capability for Non-Normal Data Using Microsoft Excel – Quality Engineering Made Simple

What you'll learn
- Recognize when process data is non-normal and why it matters in capability analysis
- Distinguish between common cause and special cause variation
- Apply strategies to eliminate special causes before conducting capability studies
- Test for normality using visual methods, the Kolmogorov-Smirnov test, and the Anderson-Darling test in Excel
- Identify and verify exponential distributions in real-world process data
- Perform capability analysis for exponential data using Microsoft Excel functions
- Use the empirical percentile method to assess capability for other non-normal and non-exponential datasets
- Interpret and communicate capability results clearly to stakeholders
- Apply both statistical theory and practical Excel skills to real-world quality and reliability problems

Requirements
- Basic understanding of descriptive statistics (mean, median, standard deviation)
- Familiarity with Microsoft Excel (entering formulas, creating charts, sorting data)
- General awareness of process capability concepts (Cp, Cpk, Pp, Ppk) is helpful but not required
- Access to Microsoft Excel (2016 or later recommended)
- Willingness to work through numerical examples and apply concepts to real data

Description
Most process capability tools assume your data follows a normal distribution—but in the real world, that’s often not the case. Many processes produce skewed, multi-modal, or otherwise non-normal data. Applying traditional capability analysis methods without checking this assumption can lead to misleading results and costly decisions.

Process Capability Analysis for Non-Normal Data Using Excelgives you a clear, step-by-step method for accurately assessing process capability when your data is not normal. This course blends “on paper” statistical explanations with hands-on Excel demonstrations so you’ll not only understand the theory—you’ll be able to apply it immediately to your own data.

You’ll learn how to:

Recognize the difference between common and special cause variation

Eliminate special causes before conducting capability analysis

Test for normality using visual methods, the Kolmogorov-Smirnov test, and the Anderson-Darling test

Identify and verify exponential distributions in process data

Perform capability analysis for exponential data using Excel’s built-in functions

Apply the Empirical Percentile Method as a robust, general-purpose approach for other non-normal and non-exponential data sets

Why take this course?

Learn from industry-leading quality engineering professionals with decades of real-world experience in manufacturing, reliability, and data analysis

Gain both statistical understanding and practical Excel skills you can use immediately in your work

Access OVER 65 downloadable Excel templates to save time and ensure accurate results

BONUS Glossary of Terminology covering all key terms related to nonnormal capability analysis

Work throughmany realistic, industry-based examplesthat mirror the challenges you face on the job

Earn aCertificate of Completionto showcase your skills to employers and colleagues

Benefits of enrolling in a Udemy course:

Lifetime access — revisit the course anytime as your career grows

Learn at your own pace — start, stop, and review lessons as often as you need

Mobile and TV access — learn anywhere, on any device

Downloadable resources — keep the tools and templates forever

Periodic discount coupons for all Manufacturing Academy courses - save a bundle

This course is ideal forquality engineers, reliability engineers, data analysts, and technical professionalswho want to make better, more data-driven process improvement decisions—without having to purchase specialized statistical software.

Who this course is for:
- Quality Engineers, Reliability Engineers, Data Analysts, Manufacturing Engineers, Process Engineers
- Quality Assurance Specialists, Six Sigma Green Belts, Six Sigma Black Belts, Quality Managers
- Operations Managers, Industrial Engineers, Process Improvement Specialists, Supplier Quality Engineers, Statistical Analysts
- Quality Technicians, Production Supervisors, Test Engineers, Business Analysts, Continuous Improvement Managers, Metrology Specialists Ask ChatGPT
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