Is Poisson Distribution Relevant for Analyzing 19th Century Cavalry Deaths?

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SUMMARY

The discussion centers on applying the Poisson distribution to analyze cavalry death reports from the Prussian Army during the 19th Century. The data consists of 109 reports of zero deaths, 65 reports of one death, 22 reports of two deaths, 3 reports of three deaths, and 1 report of four deaths, collected over a 20-year period from 10 cavalry corps. To demonstrate consistency with the Poisson distribution, one must calculate the expected mean value by multiplying the total number of deaths by the probability of each outcome, then plot the distribution using a graphing calculator or online tool. If the plotted data aligns with the Poisson distribution, the analysis confirms its relevance.

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Doing Physics at University and I have never done poisson distributions.
How the hell do I do it?
The question is...
Show that the data on the number of cavalry deaths in the Prussian Army in the 19th Century are consistent with the poisson probability distribution. The date were accumulated from reports received from 10 separate cavalry corps, yearly, over a 20 year period.
There were: 109 Reports of ZERO deaths, 65 reports of 1 death, 22 reports of 2 deaths, 3 reports of 3 deaths and 1 report of 4 deaths.

Where do I begin? Does it involve drawing a graph?!
 
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Yes, it does involve drawing a graph. To begin, you need to calculate the expected mean value of the data. This can be done by multiplying the total number of cavalry deaths by the probability of each outcome. For example, 109 x 0% (probability of zero deaths) = 0. Then add up all the expected values to get the mean. Once you have the mean, you can plot the poisson distribution for the given mean on a graph using a graphing calculator or an online graphing tool. The graph should show the probability of each outcome (e.g. 0, 1, 2, 3, 4 deaths) on the y-axis and the number of reports on the x-axis. If the data points match up to the poisson distribution, then it is consistent with the distribution.
 

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