29 Aug Computer Simulation
The objectives of this assignment are to
· provide you with experience using concepts of proportionality and geometric similarity to derive a real-world model
· provide you with experience in graphically fitting data to different models, and evaluating
The assignment is based on the following data for a sampling of black bears, consisting of their length (cm) and their weight (kg)
| Length (cm) | Weight (kg) |
| 351 | 27 |
| 422 | 70 |
| 457 | 99 |
| 329 | 47 |
| 381 | 50 |
| 335 | 34 |
| 376 | 47 |
| 356 | 41 |
| 348 | 34 |
| 378 | 52 |
| 259 | 16 |
| 439 | 99 |
| 265 | 15 |
| 351 | 41 |
| 367 | 36 |
| 417 | 81 |
| 328 | 35 |
| 401 | 54 |
| 381 | 45 |
| 361 | 45 |
Task 1 – showing the details of your work, you should use principles of proportionality and geometric similarity (you may assume that black bears are geometrically similar to each other) to derive a reasonable model relating their length to their weight. Then, using methods demonstrated in class, you should graphically derive coefficients for your model and test the fit of the model to the data as follows
· Create a scatterplot for the original data
· Create a scatterplot for the data on axes that represent the model you have derived
· Graphically (e.g. using a ruler and your eye) fit a line through the transformed data and use this line to estimate the coefficients of the model that you derived
· Using a scatterplot of the original data again, plot the curve that represents your model
· Under the assumption that your model represents a best-fit of the data (it won’t), compute the upper and lower bounds for the Chebyshev criteria, as presented in class and Section 3.2 of your text.
Task 2 – assume that someone has come up with the model for the bear data. Evaluate it exactly as you evaluated your own model in Task 1.
Task 3 – assume that someone has come up with the model for the bear data. Evaluate it exactly as you evaluated your own model in Task 1.
You are not required to use a Jupyter notebook for this assignment, but you may find it to be convenient.
As always, your report should be very professional with meaningfully labeled plots and equations and a clear description of your methods.
Task 4 (optional – no extra credit) – learn how to tell the difference between black bears and grizzly bears at the Montana Fish, Wildlife & Parks Bear Identification Program site – it’s not as easy as you might think!
You should submit your report in HTML or PDF format through Moodle before the deadline (preferably long before the deadline, so that you do not suffer from any last-minute problems). It is permissible to submit “partially-completed” reports before the deadline, in case you want to make sure “something” gets submitted. Only the latest submission will be graded.
Please, turn in your report in HTML or PDF format only. Turning in a raw notebook (.ipynb) or Word document, etc is unacceptable.
Late submissions will automatically be marked as late and NOT GRADED.
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