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Binomial Distribution and its Applications

Binomial Distribution and its Applications

Dear friends, we are happy to release this video on Binomial Distribution which is one of the foundations in probability theory! In this video Hemant Urdhwareshe explains how to calculate probability using basic formula, tables and Excel. We have also explained the applicability of Binomial Distribution in various situations. Additionally, the video includes graphical representation of Binomial Distribution using Excel. The video would be useful to all those who want to learn the Binomial Distribution and take the ASQ Certification exams such as CQE, CRE, Six Sigma Black/Green Belt.

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Unlocking the Power of FMEA: A Guide to Risk-Based Decision Making

Unlocking the Power of FMEA: A Guide to Risk-Based Decision Making

By Ray Harkins and Dianna Deeney

As engineering, quality, and product design professionals, we constantly strive to ensure that our products and processes meet the highest standards of reliability and quality. In our pursuit of excellence, one indispensable tool in our arsenal is Failure Mode and Effects Analysis (FMEA). FMEA offers a structured approach to risk management, enabling us to proactively anticipate potential failures, assess their impact, and prioritize mitigation efforts accordingly. However, the practical implementation of FMEA can sometimes prove challenging. How do we navigate its complexities to derive actionable insights and make informed decisions? Let’s explore some of the key components and best practices of FMEA.

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Understanding the Chi-square Distribution

Understanding the Chi-square Distribution

Institute of Quality and Reliability is happy to release this video to understand chi-square distribution. In this video, Hemant Urdhwareshe explains the chi-square distribution with a simulation to understand it better. Chi-square is one of the basic distributions used in hypothesis tests. The video is limited to understanding of the distribution and explains how to use tables of chi-square distribution. The video will be useful for professionals who want to take the ASQ Certification exams such as CQE, Six SIgma Black or Green Belt, CMQ/OE, CRE etc.

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Key Teaching Principle #6: Stories

As covered in the first article in this series, Principles of Effective Teaching, reliability engineers, FMEA team leaders, and other quality and reliability professionals are often called upon to teach the principles of reliability or FMEA. Whether you are a student who wants to enhance your learning experience, an instructor who wants to improve teaching results, or an engineer who wishes to convey knowledge to another person, this series will offer practical knowledge and advice.

Strengthen Your Message with Stories

The shortest distance between truth and a human being is a story.  Anthony de Mello

Key Teaching Principle #6 is the instructor uses stories and personal experiences to deepen learning.

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Normal Distribution and Z Score

Normal Distribution and Z Score

Normal distribution is one of the foundation concepts in statistics and every quality and reliability professional must understand and able to apply this concept. There are already may videos on this subject, but Hemant Urdhwareshe has tried to make the concept as simple as possible in this video. Hemant has explained the history and basics of normal distribution and its properties. Hemant has further explained the concept of standard normal distribution and how to apply it to solve some typical problems using tables of standard normal distribution. You can download the tables from www.world-class-quality.com.

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What Price Kaplan-Meier Reliability?

What Price Kaplan-Meier Reliability?

The Kaplan-Meier estimator is the maximum likelihood, nonparametric reliability estimator for censored, grouped lifetime data. It’s traditional. It’s in statistical software. Greenwood’s variance formula is well known. Could Kaplan-Meier be improved: smaller variance, better actuarial forecasts, seasonality, separate cohort variability from reliability? Could you estimate reliability without life data and preserve privacy?

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Student Questions from My Root Cause Analysis Class, Part 3

Student Questions from My Root Cause Analysis Class, Part 3

In this third and final installment in this series showcasing the most thought-provoking questions I’ve received from students of my online Root Cause Analysis class over the past five years, you will see a question each about the cause-and-effect diagram, capability analysis, and team building. This diverse set of questions, like the questions presented in the first two installments of this series, point plainly to the diversity of skills needed to become an effective quality or reliability professional.

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Reliability Growth

Reliability Growth

Concepts, Strategy, Duane Model and Application Case Study

We are happy to release this video on Reliability Growth which is a very important strategy to assure reliability of new products. In this video, Hemant Urdhwareshe has explained the basic concepts and strategy of reliability growth, the related mathematical relationships, and the Duane Model with an application case study of introduction of new model of Diesel engine. Hemant is a Fellow of ASQ and is ASQ CRE, CMBB, CSSBB, CQE and CMQ/OE. We are sure that viewers will find it useful. If you like the video, do not forget to click the like button.

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Hazard Plotting Approach and Case Study

Hazard Plotting Approach and Case Study

to analyze failure data of shock absorbers

In this video, Hemant Urdhwareshe explains how to use Hazard Plotting to estimate parameters of Weibull Distribution using Excel with an application case study. Hemant also explains related mathematical concepts in Hazard plotting. Hemant is a Fellow of ASQ, and is certified by ASQ as CRE, CMBB, CSSBB, CQE and CMQ/OE. Viewers may like to watch our previous video on Weibull Probability Plotting.

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DOE-5: Fractional Factorial Designs, Confounding and Resolution Codes

DOE-5: Fractional Factorial Designs, Confounding and Resolution Codes

In this video, Hemant Urdhwareshe explains basic concepts of Fractional Factorial Design, Confounding or Aliasing and Resolution of designs. View earlier videos in this series: Introduction, Application, Coded and Uncoded Values, and a Case study.

Hemant is a Fellow of American Society for Quality (ASQ) and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Reliability Engineer (CRE), Quality Engineer (CQE) and Manager of Quality and Organizational Excellence (CMQ/OE).

The video would be useful to all those who wish to learn about use of fractional factorial designs to reduce number of runs in experiments. It will be also be greatly useful for preparing ASQ certification exams!

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Semi-Nonparametric Reliability Estimation and Seasonal Forecasts

Semi-Nonparametric Reliability Estimation and Seasonal Forecasts

I estimated actuarial failure rates, made actuarial forecasts, and recommended stock levels for automotive aftermarket stores. I wondered how to account for seasonality in their sales? Time series forecasts account for seasonality but not for age, the force of mortality accounted for by actuarial forecasts. I finally figured out how to seasonally adjust actuarial forecasts. It’s the same method, David Cox’ “Proportional Hazards” model, used to make “Semi-Parametric” estimates and “Credible Reliability Predictions”.

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Student Questions from My Root Cause Analysis Class, Part 2

Student Questions from My Root Cause Analysis Class, Part 2

In part two of this three-part article series, I am continuing to showcase some of the best questions I’ve received over the past 5 years from students of my online Root Cause Analysis class, along with my answer to them. 

And as Indira Gandhi once said, “The power to question is the basis of all human progress.” And so, it is my hope that by sharing these questions and answers, others may all learn and progress as well.

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Key Teaching Principle #5 Convey Interest and Passion

As covered in the first article in this series, Principles of Effective Teaching, reliability engineers, FMEA team leaders, and other quality and reliability professionals are often called upon to teach the principles of reliability or FMEA. Whether you are a student who wants to enhance your learning experience, an instructor who wants to improve teaching results, or an engineer who wishes to convey knowledge to another person, this series will offer practical knowledge and advice.

Conveying Interest and Passion When Teaching

“A teacher who is attempting to teach without inspiring the pupil with a desire to learn is hammering on cold iron.” Horace Mann

What is Interest and how does it enhance learning?

The definition of interest is “a feeling that accompanies or causes special attention to something or someone.” [Read more…]

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DOE-4: Case Study in Design of Experiments to Maximize Fatigue Strength of Crankshaft

DOE-4: Case Study in Design of Experiments to Maximize Fatigue Strength of Crankshaft

Hemant Urdhwareshe, Director of Institute of Quality and Reliability presents case study to maximize fatigue strength of crankshaft using design of experiments using SigmaXL Template of Full Factorial Design with three factors each at two levels.

Hemant is Fellow of ASQ and possesses five certifications from ASQ including Six Sigma Master Black Belt and Reliability Engineer. It is recommended that viewers first watch the previous videos DOE-1, DOE-2 and DOE-3 before watching this video. SigmaXL trial version can be downloaded from www.sigmaxl.com.

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DOE-3: Coded and Uncoded Values & Establishing Regression Equation

DOE-3: Coded and Uncoded Values & Establishing Regression Equation

I am happy to share my third video on Design of Experiments (DOE-3). This is the third video in our series on Design of Experiments. It is recommended that viewers should watch the first two videos (doe-1-introduction-to-design-of-experiments and DOE-2: DOE Case Study of Spot Welding) before watching this video for better learning and understanding.

In this video, we have explained how to convert uncoded values of factor-levels to coded values and vice versa. We have also explained how to create a linear regression equation from the results of a full factorial two-level experiment.

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