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A listing in reverse chronological order of articles by:
Dear friends, we are happy to release this second video on Hypothesis Testing! In this video, Hemant Urdhwareshe explains One Sample t-test along with illustrations of Student’s t-distribution. Hemant has also explained the concept of degrees of freedom and p-value in this video.
We recommend viewers to watch Hypothesis Testing, Part-1 video before this video.
We are sure, you will find this useful!
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As an industrial statistics consultant for the past 25 years, I have frequently fielded questions related to sample size determination. Unfortunately, I have encountered many instances where simple rules of thumb were used for any purpose (like always use 30). Sample size guidance really depends on what the goal of the study is, the type of data we are dealing with, what statistical method we are using and some other factors as well. Common activities which typically require sample size determination include:
All these applications require different assumptions and calculations to determine an appropriate sample size. In this article, we focus on Sample Size determination for Hypothesis Testing. It is assumed that the reader is already familiar with Hypothesis Testing.
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Dear friends, Our best wishes to all for a great Quality Month and Year Ahead for your Quality Initiatives! In this Quality Month, we are starting a new series of videos on Hypothesis Testing in our Channel! we are happy to release our first video on Hypothesis Testing! We will be releasing a complete series of videos on Hypothesis Tests! In this first video on the subject, Hemant Urdhwareshe explains the basic concepts and discusses an illustration of One-Sample Z-Test!
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Data-driven decision-making is central to designing and improving products and processes. Professionals are often presented with statistical analyses, with key outputs such as p-values or confidence intervals that indicate whether results are “statistically significant.” However, statistical significance doesn’t always translate into meaningful changes on the shop floor or within a product’s design. Understanding the difference between statistical significance and practical significance is crucial to making well-informed decisions that genuinely impact the business.
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Stability studies are used to understand and model the degradation of key product characteristics over time. They are often used to determine the product’s shelf life (the length of time a product may be stored without becoming unfit for use or consumption).
Shelf-Life studies should identify the potential “failure modes” and how they will assessed/ measured. Examples of characteristics that are measured often include appearance attributes, texture, taste, microbial counts, and product effectiveness/performance.
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Dear friends, we are happy to release this video on Acceptance Sampling plans for Attributes. In the video, Hemant Urdhwareshe explains how to select appropriate sample size using Sampling Plans such as MIL-STd-105E, ANSI/ASQ Z1.4, IS 2500 Part-1 (or ISO2959 -1). The video also explains interpretation of Operating Characteristics from the standards and in Microsoft Excel. Additionally, Hemant also illustrates how to generate a Sampling Plan in Minitab software.
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In my 30-year career as an Industrial Statistics consultant, I have frequently been told by clients that they have performed Design of Experiments (DOEs), to try and resolve design or manufacturing issues. What has become clear is that many engineers and scientists apply a rather liberal definition to DOE and include any type of experimentation in what they deem to be “DOE”.
The reality is, simplistic or haphazard “experiments” rarely are effective in solving problems, especially complex ones. Statistically based DOE provides several advantages over more simplistic approaches such “trial and error” or “one-factor-at-a-time” experimentation. These advantages include:
Each of these advantages are discussed in a bit more detail below.
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Dear friends, I am happy to share our first video on Quality Control Acceptance Sampling Plans! In this video, I have explained some basic concepts and terminology of sampling plans. I have also illustrated use of Microsoft Excel to construct Operating Characteristic Curve and AOQ Curve of a sampling plan It is not possible to inspect 100% parts received from suppliers. Obviously, processes need to be capable to produce consistently good quality parts that conform to the specifications. However, there are quite a few processes where the capabilities are either marginal or low. Also, controls at suppliers may not be adequate due to many reasons. Therefore there is still a need for statistical Acceptance Sampling Plans.
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In this third and final installment in this series, I continue my review of questions I’ve received from students of my online course titled, “Statistical Process Control (SPC) Using Microsoft Excel.
The length of the course is just under 11 hours, and covers a wide range of topics under four major chapters: Pareto Analysis, Control Charting, Process Capability Analysis, and Linear Regression. In it, I draw numerous case studies and examples from my career in quality management and manufacturing engineering. These real-life examples, I believe, are what spark the most questions. As the statistical approaches are placed in the context of plausible scenarios – scenarios the students routinely see themselves – the content takes a better grip and leads the student toward a greater desire to learn.
So please enjoy this last set of questions. Maybe they will inspire you as well.
[Read more…]“Success depends upon previous preparation, and without such preparation there is sure to be failure.” Confucius
Like any task in life, it is essential that instructors prepare well for each course being taught.
Dear friends, I am happy to release this video on reliability analysis of life data with multiple failure modes. The analysis procedure is illustrated fully in Minitab software with a real-life case study with six failure modes. The concept is that when a system fails for one failure mode at time t, this data of time is right censored data for other failure modes! Analysis in Minitab can be performed in one single command!
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Dear Friends, I am happy to release this video on a very important and fundamental statistical concept of Central Limit Theorem (CLT). The CLT forms basic for statistical control charts, hypothesis tests, ANOVA etc. I am sure, you will find the video useful!
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The Kaplan-Meier reliability estimator errs on Fred’s bicycle ships and failure data! The Kaplan-Meier estimate was computed from Fred’s bicycles’ grouped failure data in the body of a “Nevada” table. It disagrees with the reliability estimate from ships cohorts and monthly failures (without knowing which cohort the failures came from). It disagrees with least squares nonparametric reliability estimates. All but the Kaplan-Meier estimate agree! Which would you prefer?
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In this second installment of my three-part article series, I am again showcasing some of my favorite student questions from the past seven years since first launching my online class titled “Statistical Process Control (SPC) Using Microsoft Excel.”
Each exchange represents not only the question in that student’s mind, but the type of question that lingers the minds of countless professionals trying to advance their skillsets. Each exchange – some of which I edited for clarity – become a permanent part of the class itself that future students can read and learn from. Perhaps you’ve had some of these questions as well.
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Dear friends, In this video, Hemant Urdhwareshe explains the concepts of linearity, center point and orthogonality in Experimental Designs! The video is created to understand these concepts with simple illustrations! We are sure, you will find this video useful!
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