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If you're loading in the data to Mathematica, one tip is how to deal with missing values (some of the maxes or mins are missing):
LinearModelFit[Cases[data, {_?NumericQ ..}], {x}, x]
Runs linear regression on only those cases of data that are not missing -- only on those cases that are numeric data.
These are the sinusoidal models of BG Climate Normals, by month (max, mean, min temperatures), plus a model for all the 1893-1992 record temperatures taken from the Fletcher data.
Fletcher stuff may appear as well. You should try to be up to speed on everything we've been doing, especially since Spring Break. I know that it's been rougher for some of you than for others.
There may be an InsightMaker component. That would seem reasonable, given all we've done.
This is an interesting one. When I started looking for Covid-19 models, I found this one InsightMaker, and it incorporated real data.
As applied math modelers, we need to try to make sense of real data (e.g. the Fletcher data). In this lab we examine how some modelers tried to make sense of real data (and make important predictions); then, how we need to make changes to their model, to reflect the real world, which intruded upon their theories....
At this point, I've got the predator/prey video summaries done:
Here are the SIR lab summaries:
Again, I'll try to do some video summaries to emphasize and clarify some things.
Some good links that I might recommend (a few of which we'll focus on):
In particular, we will implement The SIR Model for Spread of Disease - The Differential Equation Model in InsightMaker.
(An excellent introduction to SIR models, from both the infectious disease and mathematical sides)
Questions:
This on-line estimator (i.e., a model!) allows one to estimate deaths, as well as death by age-category.