PROGRAMMING WARMUP AND BASIC ERROR ANALYSIS
For problems 3 and 4 below, please put all your results into a single Jupyter notebook along with a discussion. Download
the Jupyter notebook as ipynb from the file menu (file->Download as) and upload that to Canvas. No exceptions.
Problem 1 (10 pts)
Fill out this survey: https://forms.gle/5HRFTMiLeJTpxvUx5. You do not need to submit anything beyond the survey for this
Problem 2 (10 pts)
Go through the EasyPy slides online www.github.com/saadtony/chempy and attempt to replicate those in your own python
notebook. You don’t need to submit anything for this question. Note that, if you do not have a local installation of Python
(via Anaconda), then feel free to use: juno.chpc.utah.edu. Use your UNID and password to access the service.
Problem 3 (40 pts)
The second derivative of a function f(x) can be approximated numerically as
where h is a value set by the user that determines the accuracy of the derivative. Larger values in h are expected to produce
large errors while smaller values of h will produce consistently smaller errors.
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