Here is a Challenge: Life Data Analysis
Some years ago a few colleagues compared notes on results of a Weibull analysis. Interesting we all started with the same data and got different results.
After a recent article on the many ways to accomplish data analysis, Larry mentioned that all one needs is shipments and returns to perform field data analysis.
This got me thinking: What are our common methods and sets of results when we perform life data analysis?
The Life Data Analysis Challenge
So, here’s a challenge: Given the data in this life-data-challenge.csv file, perform an analysis to answer two questions:
- How many returns should we expect next month?
Is the rate or returns increasing or decreasing?
3 [Bonus question] Based on your analysis and experience, what questions should we answer next?
Here is the data, life-data-challenge.csv
Notes About the Data
It is made up data and kept relatively simple for the purpose of allowing a wide range of analysis approaches. The data represent the time to failure in days. The count of days are from shipment till the day, including weekends and holidays, the customer reported the failure.
The item is a battery powered portable hand drill for use by a home workshop or woodworking enthusiast. In other words, not a contractor. The drill is used sporadically for a wide range of uses and situations around a persons home, office, or workshop.
To keep things very simple there were 1,000 units shipped on one day and the failure data is all from that one day of shipments. Not all units have failed, only 75 have failed.
The data is in one column and not sorted nor in any particular order.
Reporting Your Results
There are two main points in this challenge.
First, please answer the two (three) challenge questions based on your analysis. Provide a summary of your analysis, graphics, charts, or what ever makes sense for us (me and your peers) to understand your results and how to you got them.
Second, please comment on what, if any, assumptions you made for your analysis. For example, if you assume the data is exponentially distributed (please, I really hope not!), list that as an assumption.
Third, I really do have a problem with keeping to two points today, please comment on what additional information you would like to have available, if any, to improve your analysis.
Please add your results to the comments section below, or email them to me (Fred) at email@example.com
That is the challenge. Looking forward to your results and analysis.
Thanks for taking part and enjoy.