Subscribe to Blog via Email
© Mark Biegert and Math Encounters, 2018. Publication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Mark Biegert and Math Encounters with appropriate and specific direction to the original content.
DisclaimerAll content provided on the mathscinotes.com blog is for informational purposes only. The owner of this blog makes no representations as to the accuracy or completeness of any information on this site or found by following any link on this site. The owner of mathscinotes.com will not be liable for any errors or omissions in this information nor for the availability of this information. The owner will not be liable for any losses, injuries, or damages from the display or use of this information.
Monthly Archives: December 2018
I have been working through the book Collect, Combine and Transform Data Using Power Query in Excel and Power BI by Gil Raviv – it is an excellent Power Query (PQ) resource. I particularly like the methods discussed in Chapter 10, which focused on how to make your queries robust, that is, insensitive to minor deviations in the input data. Chapter 10 spoke to me, and I immediately began looking for some practice data that suffered from common inconsistencies: headings in different cases, minor spelling errors in the data body, and inconsistent wording (example, "Co." instead of "Company"). I found that data in the Wikipedia's information on US WW2 cruisers. In this post, I will look at the production of cruisers by the US during WW2. See Figure 1 for a typical example of a WW2 US light cruiser. Continue reading
One WW2 topic that continues to intrigue me was how US war planners kept the Imperial Japanese Navy (IJN) at bay long enough to build a large naval force. The key was the use of submarines for commerce raiding to disrupt the war material supply chain and tie down Japanese surface forces with convoy defense duty. This post will use Power Query to scrape the Wikipedia for this data. The Wikipedia is becoming a wonderful source for WW2 information. Continue reading
I spend quite a bit of time at a cabin I have built in northern Minnesota. Technically, I spend most of my time in the garage on the site and I have decided that I need to be able to watch the local television stations in Duluth. These stations are ~75 miles away and I need to determine the bearing along which to point my antenna. This seemed like a good Excel exercise that I can also use as an example for those I tutor at the Hennepin County Library. There are web calculators available that perform this calculation (example), but it is more fun doing it myself. Continue reading