Days Postings
Blog Series
Subscribe to Blog via Email
Copyright Notice
© Mark Biegert and Math Encounters, 2020. 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.
Disclaimer
All 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.
Author Archives: mathscinotes
Visualizing House of Representative Data Using Power Query
I have been following certain bills through the US House of Representatives and wanted to know how the voting varied by US regions and political parties. I also want to generate tables that show how my state's representatives vote (Figure 1). Fortunately, the votes are documented online and Power Query was able to easily grab and process the data. Continue reading
Posted in Civics Through Spreadsheets, Excel
Leave a comment
Power Query Regular Expression Hack
I use Python, R, and Excel every day in the course of my work. Because many corporations are focused on the Microsoft Office suite of tools, many businesses require that I use Excel/Power Query so that they can work with the tools I develop after I am done. Fortunately, I really enjoy using Power Query, but I find it irritating that it does not support regular expressions. I must admit that Power Query's standard functions can do a good job of extracting strings, but the process is a bit tedious. However, I have a large library of regular expressions for extracting email addresses, phone numbers, social security numbers, and the like that would be efficient for me to use. Continue reading
Posted in Excel
Leave a comment
US Navy WW2 Aviation Statistics Cleanup Using Power Query
was reading a forum post on fighter kill ratios during WW2 and decide to compute some Imperial Japanese Navy (IJN) vs US Navy (USN) ratios for myself. I should point out that these ratios are generally viewed as inflated because of the difficulty of confirming downed aircraft. However, the inflated numbers continue to be quoted. The published reports state that the F6F Hellcat had the best kill ratio of the USN/Marine fighter at 19-to-1, followed by the F4U Corsair at 11-to-1, and the F4F Wildcat at 7-to-1. Continue reading
Posted in History Through Spreadsheets, Naval History
2 Comments
Battleship Shell Size Comparison
During some routine research on battleships, I encountered some photos on the web that I thought were worth sharing here. My hope with these photos is to give readers a feel for the size of these guns and their projectiles. Continue reading
Posted in Ballistics, Naval History
1 Comment
Cleaning Up Coffee Production Data Using Power Query
I recently volunteered to do some pro-bono data analysis and front-end web development for a very nice Kenyan woman who is trying to provide a US distribution channel for some coffee growers in her native Kenya. This post documents my work on tidying her coffee data. Continue reading
Converting a Binary Table To Hex With Power Query
I was recently given a specification for a Digital-to-Analog Converter (DAC) that contains a table of register settings that correspond to the DAC's output voltage. Each row of the table corresponds to one register setting and each column corresponds to a register bit. Thus, each table cell corresponds to a single bit – a 1 or 0. A simplified version of this table is shown in Figure 1 – a simplified version of the much larger table I was working with. Continue reading
Posted in Excel
2 Comments
Steinhart-Hart Thermistor Calibration in Excel Using Matrix Formulas
I had a job this week that required that I use the Steinhart-Hart equation for modeling the thermistor resistance versus temperature relationship. The requirement was driven by the customer's need for high accuracy. Most thermistor applications do not demand high accuracy, but this application can tolerate no more than ±0.2 °C of error. This means that I cannot use the β-based thermistor model, which in this application would have an error of more than ±2 °C. This page will show how how to perform an efficient 3-point calibration using Excel and a bit of matrix math. As a side benefit, I am using this workbook as an example of matrix math in my Excel tutoring at a local library. Continue reading
Posted in Electronics
2 Comments
Another Excel TEXTJOIN Example
I have been analyzing seemingly random fuse failures in different products. The failing fuse is similar to the unit shown in Figure 1. My analysis has shown that the fuses are failing because of damage they are sustaining during the manufacturing process. In my analysis report, I was asked to list each product that experienced a fuse failure and to list how many days each product was in service before the fuse failure occurred. Continue reading
Battleship Classes and Throw Weights
I just finished reading The Battle of Surigao Strait by Anthony Tully, a battle that saw the final clash of battleships. For a battleship aficionado, the climax of the fight was the contest between two Japanese battleships and six US battleships, where five of the six US battleships had been sunk or heavily damaged during the Pearl Harbor attack – only the USS Mississippi had escaped the carnage of Pearl Harbor. These were old battleships (Table 1) with two having been commissioned during WW1 and the rest shortly after the WW1 ended. Continue reading
Everest Climbers With No Supplemental Oxygen
I recently have seen pictures on the news of a line of people preparing to summit Mount Everest (Figure 1), which got me thinking about the difficulty of waiting in line under low-pressure conditions.The vast majority of the people who climb Everest use supplemental oxygen. The air pressure at the summit of Everest is about 0.3 atmosphere, which is not enough to support human life for an extended period of time. But a relatively small number of people have climbed Everest with No Supplemental Oxygen (NSO). In this post, I will look at this very select group of people. Continue reading
Posted in History Through Spreadsheets
Leave a comment