Difference between revisions of "EGR 103/Spring 2020/Lab 7"
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* See [[Python:Fitting#General_Linear_Regression]] | * See [[Python:Fitting#General_Linear_Regression]] | ||
Revision as of 06:02, 25 March 2020
The following document is meant as an outline of what is covered in this assignment.
Contents
Additional References
- Python args and kwargs: Demystified at Real Python
- Draft tutorial:
Typographical Errors
None yet!
Specific Problems
- Be sure to put the appropriate version of the honor code -- if you use the examples from Pundit, the original author is either DukeEgr93 or Michael R. Gustafson II depending on how you want to cite things.
Chapra 14.5
- See Python:Fitting#Polynomial_Fitting
- Be sure to also calculate and report \(s_{y/x}\) and \(r\).
Chapra 14.7
- See Python:Fitting#General_Linear_Regression
- Whenever you have values on an axis that makes the axis numbers take up more space that they should, you can tell Python to use scientific notation on that axis. For this code, you will want to use scientific notation on the y axis; you can do this with the code:
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
- Be sure to calculate the R value. Note that it is not the same as the slope of the line you would get if you try to model $$p$$ as a function of $$T$$.
Chapra 14.27
- See Python:Fitting#Polynomial_Fitting and Python:Fitting#General_Linear_Regression
- The reason for having the dashed line plotted after the solid one is because the two lines are somewhat similar and this will let you see the first one in the gaps of the second one.
- Be careful about checking what Connect is asking for!
Chapra 15.10
Chapra 15.11
- See Python:Fitting#Nonlinear_Regression
- For the initial guesses, make sure you understand the subscripts for the parameters and then figure out how to approximate their values from the information provided in the problem.
Chapra 15.22
- See Python:Fitting#Nonlinear_Regression
- For the initial guesses, mainly note that choosing $$b=0$$ would be a terrible idea.
Chapra 15.29
- See Python:Fitting#Nonlinear_Regression
- Note that the independent variable will be the temperature in K and the dependent value will be the natural log of the pressure. That is also what you will be graphing.
