Reliability Engineering Statistics (2026) (Udemy.com)

Welcome to the Manufacturing Academy's Reliability Engineering Statistics (2026) … the one course you need to propel your career in reliability forward!

Reliability is the ability of a product, system, or process to perform its intended function without failure over a specified period of time. Reliability engineering focuses on the analysis and management of factors that affect product performance, ensuring systems function as intended and minimizing the risk of failures.

As industries increasingly prioritize quality and performance, the demand for reliability engineers has grown. These professionals play a critical role in product design by ensuring that products are durable and perform consistently over time. They are also key drivers of process optimization, using data-driven insights to enhance efficiency and reduce variability.

What You’ll Learn:

Based on the American Society of Quality's (ASQ) Body of Knowledge for the Certified Reliability Engineer (CRE) exam, 2024 edition, this course steps through the foundational statistical tools and methods used by leading reliability engineers to make informed decisions and improve product and process reliability.

Comprehensive coverage of the following 8 major areas of Reliability Engineering Statistics including:

1. Basic statistics: Population, parameter, sample, and statistic, the central limit theorem, methods to estimate and interpret statistical values.

2. Probability concepts: Independence, mutually exclusive events, conditional probability, probability tools such as probability trees and expected frequency trees.

3. Probability distributions and functions: Statistical distributions such as binomial, Poisson, exponential, Weibull, normal, log-normal, chi-square, and Student’s t distribution, and how to use them to analyze reliability data. How to interpret probability plots for each continuous distribution. Goodness of fit to a distribution. The family of probability functions including PDF, CDF, reliability, and hazard functions and how they interrelate.

4. Sampling plans: Theories, tables, and formulas to determine appropriate sample sizes.

5. Hypothesis Testing: Hypothesis testing with t and Z distributions, error types, paired comparison tests, F tests, testing for proportions, and more.

6. Nonparametric Statistics: Mann-Whitney test, Kruskal-Wallis test, Mood’s Median test, Wicoxon Signed Rank test, dealing with censored data, and Kaplan-Meier test.

7. Statistical process control (SPC): Control charting, capability studies, Cp, Cpk, Pp, and Ppk.

8. Confidence and Tolerance intervals: Confidence intervals, tolerance intervals, Assessing the use of confidence and tolerance intervals for reliability analysis using Weibull, normal, and lognormal distributions.

So whether you're working your way into a career in reliability engineering, studying for the ASQ Certified Reliability Engineer (CRE) exam, or simply aiming to expand your knowledge base in product design and engineering, our Reliability Engineering Statistics (2026) course has what you need!

New Topics for the 2026 Edition of the Course:

• Geometric Distribution

• Hypergeometric Distribution

• Anderson Darling Test of Normality

• Nonparametric Methods like the Sign, Wilcoxon-Mann, Friedman Tests and More

• All associated Excel templates

Who Should Enroll:

• Reliability Engineers: If you're already working in reliability engineering and want to refine your statistical skills, this course will help you deepen your expertise in analyzing and optimizing system performance.

• Quality Engineers and Managers: Professionals focused on ensuring product quality will benefit from understanding how reliability statistics can support long-term quality and minimize defects in products and processes.

• Manufacturing and Process Engineers: Those involved in process optimization and system design will find valuable tools for improving operational efficiency and product reliability.

• Product Designers and Development Engineers: Engineers working on product development will gain insights into how to incorporate reliability into the design phase to enhance product longevity and reduce the likelihood of failure.

• Data Analysts and Engineers: If you have a background in data analysis and want to apply your skills to the realm of reliability engineering, this course will introduce you to specialized statistical techniques used in the field.

Whether you're just starting out or are seeking to expand your skillset, this course will provide practical, data-driven methods for enhancing reliability across a wide range of industries.

What You Get:

• 9+ hours of rich video-based instruction (OVER 120 lectures!!)

• A CRICITICAL RESOURCE for professionals preparing to take the American Society for Quality’s (ASQ's) 2024 edition of the Certified Reliability Engineer (CRE) exam

• 60 downloadable resources including Excel templates, "cheat sheets", and an assortment of useful diagrams and tables.

• LOTS OF Practice Exercises at the end of each section and an exam at the end covering reliability engineering statistics concepts and terms.

• A Glossary of Terminology with detailed definitions covering not only the major ideas of engineering statistics, but also key ideas from the broader field of reliability.

• LIFETIME ACCESS to all course materials AND all other materials we may add later.

• A personalized Certificate of Completion with your name, the course's name, and the time duration of the course (useful for fulfilling the need of some CEU requirements)

• Q&A access through the Udemy platform to a 30+ year manufacturing, quality, engineering, and business professionals.

What did other students say about our Reliability Engineering Statistics course?

• "I love how all different concepts are combined into a single course with wonderful examples and how each concept differs from the other" - N. Dubey

• "I can't really think of a better and practical short course regarding Reliability Statistics than this, thank you very much for the content" - A. Dutta

• "Well documented, well presented and generously resourced." - P. Dzitac