Saturday, June 22, 2024

Modeling with Dog Food

Tomorrow is the end of the Summer 1 Term and I took my final this morning.   My next two classes officially start on Monday, so I'm looking forward to those.

As I mentioned in my last post, I am taking a Math class each term, so the one I just finished was on Math Problem Solving and had an emphasis in Modeling.  I'm trying to blog about some of the things I learned so I don't forget them!

In the first week, while we were learning the difference between modeling mathematics and mathematical modeling, our written assignment consisted of a prompt with an image like this one:

Back of a Royal Canin dog food bag
Screenshot from https://www.chewy.com/royal-canin-breed-health-nutrition/dp/33917

In the assignment, some information was given about some dogs and we were asked to answer several questions using a chart like the one above.  We were also asked to write a personal perspective regarding the assignment and how we might use it in our classrooms.

When I originally had worked through the assignment, I guess-timated how much a dog that wasn't on the chart might eat based on the provided information.  However, as I started to reflect on how I might use this in my classroom, I wondered if I could use regression analysis to determine a mathematical relationship between the weight of the dog and the amount being suggested and use that to predict the feeding amount for a dog not on the chart instead of using a rough estimate.  

I put in the weight of the dog in pounds for the x-variable and the cups of dry food for the y-variable and did 3 different table of values, one for each activity level.  Check out those lovely linear patterns...



One of the things I really enjoyed about this class was the idea of using mathematics to model the world around us.  I don't have any dogs, so I would have never known a chart like this existed, but it is now tucked into my regression unit for statistics.

If you have any gems like this one that you are willing to share, please let me know!

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