Data Analysis Assumptions
Abstract
Greg and Fred discuss the importance and context of assumptions in data analysis.
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Your Reliability Engineering Professional Development Site
by Greg Hutchins Leave a Comment
Greg and Fred discuss the importance and context of assumptions in data analysis.
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by Hemant Urdhwareshe Leave a Comment
I am happy to share my next video on ‘Warranty Data Analysis using Minitab Software’. The video explains the popular Nevada format of warranty data collection and how to convert it in to suitable tabular format so that the same can be analysed using Minitab or other software. In this video, we explain the various types of failure data when Nevada format is used. In some of our previous videos, we have already discussed various types of life data i.e. complete, right censored, left censored and interval censored. When all these types of data are present at the same time, we call such data as multiply censored. Such type of data can be analysed on Minitab using the commands for arbitrarily censored data.
[Read more…]by Hemant Urdhwareshe Leave a Comment
I am happy to share my next video on ‘Life Data Analysis of Right Censored Data using Minitab Software’ as many viewers had requested! In this video, we revisit the types of failure data and explain procedure to analyse right censored data in Minitab software with an example.
The procedure is predominantly in three steps:
The procedure is explained in detail using an application example of camshaft failure data. In the video, I have also explained how to estimate expected number of failures by 100000 Kilometres and also the 95% upper bound which is the worst-case scenario. I am sure, you will find this video interesting and useful for practical application of Life Data Analysis! Your feedback on the video is welcome!
[Read more…]by Hemant Urdhwareshe Leave a Comment
In this video, Hemant Urdhwareshe discusses how to analyse complete data of failures to identify distribution, identify distribution parameters, estimate reliability at a specified time using Minitab (version 19) software. The file used in the video can be downloaded here: Reliability Data.xls.zip.
[Read more…]by Fred Schenkelberg Leave a Comment
Reliability engineering involves data thus requires the analysis of that data.
A spreadsheet full of data is little more than a list of numbers or text. Revealing relationships, interactions, and projects enables the data to help us understand the the world a little better.
The webinars related to data analysis explore when and how to conduct data analysis. We also explore how to select the right approach and best practices to communicate the process and results.
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These webinars intend to create value. If you learn and use the concepts and ideas in these webinars, you will create value. Please let us know how you apply the information provided or if you have any questions.
by Gabor Szabo Leave a Comment
In this episode, I am joined by the mighty David Langer, who is the founder of Dave on Data, an education company focused on teaching the world the 20% of analytics that drive 80% of ROI. Having worked for companies such as Microsoft, Data Science Dojo and Schedulicity, Dave is considered a veteran in the data science and teaching space; what really makes him stand out from others is his unique and very effective approach to teaching. Get ready as Dave and I take you on an informative and fun chat about essential data analysis skills, R programming, Process Behavior Charts in non-manufacturing environments and other pieces of his great wisdom.
by Fred Schenkelberg Leave a Comment
One of my standing searches revealed an article that has shows a nice example of reliability data analysis. The author analyzed the time-to-violent-death of Roman emperors. The article is interesting in a historical sense plus illustrates a few key points for any life data analysis.
The article, “Statistical reliability analysis for the most dangerous occupation: Roman Emperor” by Joseph Homer Saleh takes a look at the 69 Roman emperors and 62% of them that suffered a violent death. The idea of the study was to determine if there is some pattern to the deaths and if the analysis would reveal any insights for those studying the era of the Roman emperors. [Read more…]
by Christopher Jackson Leave a Comment
Chris and Fred discuss data analysis … specifically the first question we ask before we help someone with their data analysis project. Chris always asks – what is the decision that this data analysis will support? And Fred always asks – where did this data come from? The reason these questions are important is that you need to know what information you need before you construct an analysis to get that information. And you need to be confident in the results. A single data set can potentially create multiple information sets. And this depends on how you construct the analysis. Which based on the decision. Listen to this podcast if you would like to learn more.
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by James Kovacevic Leave a Comment
James and Fred discussing the use of data analysis with asset management.
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by Fred Schenkelberg Leave a Comment
Once a product is launched, it’s time to understand its performance. The design is done, and the assembly process is working. Now we can focus on answering the question: is the product hitting reliability targets or not?
Thus, we do field data analysis. [Read more…]
by Fred Schenkelberg Leave a Comment
Once a product is launched, it’s time to understand its performance. The design is done, and the assembly process is working. Now we can focus on answering the question: is the product hitting reliability targets or not?
Thus, we do field data analysis.
[Read more…]In my prior article, an overview of vehicle telematics was provided. Telematics data includes time stamped records and fields containing count or parametric data recorded from the vehicle CAN bus. The count data is always a non-negative integer and the parametric data is stored as real numbers, generally in scientific format. This article focuses on the analysis of counting data.
Count data is used to monitor events, i.e., the number of trips, the days of operation, the calendar date, door open/close cycles, the number of engine starts/stops, or other variables. So, if a variable is selected for analysis, how can it be analyzed and a vehicle be characterized? How can fleets be analyzed? Can vehicle usage percentiles be determined?
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Convince, don’t confuse! Justify, don’t exaggerate!
Project managers want to deliver their product on time and on schedule. Design engineers want to believe that they have got it right. But your analysis, test results and field data suggest that there might be a problem. What do you do?
The key words here are “suggest” and “might be”. How should you present your evidence and analysis such that it doesn’t exaggerate with certainty, or confuse with statistics? How should you ensure that your conclusions lead to positive action? [Read more…]
Ask a question or send along a comment. Please login to view and use the contact form.by Fred Schenkelberg Leave a Comment
We have data. Often, an abundance of data concerning equipment failures. Failures per month or MTBF-type measures do not reveal sufficient insights to understand the pattern of failures.
by Fred Schenkelberg Leave a Comment
We have data. Often, an abundance of data concerning equipment failures. Failures per month or MTBF-type measures do not reveal sufficient insights to understand the pattern of failures.
[Read more…]