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Abstract
Looking for an inexpensive and powerful data analysis tool? R is free. R is an open source statistical programing language. Let’s explore R’s many capabilities concerning reliability statistics from field data analysis, to statistical process control.
Detailed Information: Reliability engineering relies on reliability statistics. We need software tools that allow us to explore and model data on a regular basis. Simply plotting the data, from a histogram to a probability plot allows us to ask better questions and solve problems faster. There are commercial software packages available from general purpose statistics, JMP or MiniTab, to specialized reliability packages, Weibull++ or Reliability and Maintenance Analyst. There are versatile math packages like MathCad or Mathematica. All are expensive and provide customer support and, training. R is a statistical programing language. It’s free. It has an extensive library of speciality packages. And, an immense supportive community.
It’s powerful, capable of producing publication ready graphics, includes basic and advance statistical tools, and you only need to learn a few basics to get started. Let’s explore using R for a range of common reliability statistics problems. Plotting field data, exploring process capability with statistical process control examples will highlight the power and versatility of this amazing resource.
This Accendo Reliability webinar originally broadcast on 10 March 2015.
Hi. I received your upcoming webinars notice but, unfortunately it is unclear from the provided link how to sign up.
Please help.
Thanks
Jeff
I figured out that IE was blocking the content. I was able to log-in an register using Chrome. Disregard my earlier question.
Thanks
Dear Fred,
I just stumbled upon this event in my email inbox.
I can’t attend but wish to let you know that I regularly use R to conduct Reliability analysis and also analyse equipment performance records. My change of employment meant that other software packages were not available and/or too expensive.
I find ggplot2 and plyr to be a useful packages and I tend to keep data together as a dataframe.
I have examples of an MCF analysis I have recently completed in R if you are interested.
Regards
Glenn Sanders
South Australia
HI Glenn,
Yes, while it may take a bit of work to master R it is very useful, powerful, and free.
Please do send over an example or two of your work with R, that would be most useful for the presentation next week.
Cheers,
Fred