**Plan your solution:**

- Draw a picture, in this case, list all of your data and equations
- Remember the fundamentals and apply

- Draw your material or energy balance envelope (If necessary, not in this case)
- Remember [Accumulation = In – Out + Source/Sink]

- Think about what you need to do and the answer you want
- You need to perform linear regression, so you want a ‘least squares’ package
- You’ll want to plot the solution to get the information
- Need a regression solver (scipy.optimize), statistical support(scipy.stats) and plotting package (Matplotlib,pylab)

**How to start your program**:

- With Ipython open, open your editor
- Label your program (you’ll never remember it, go ahead and label it!)
- Import the packages you’ll need for solving

Next, input your vapor pressure data into an array so that you can begin to manipulate the data. Arrays are ‘objects’ (here is a good link) and are very flexible and useful for managing numbers.

**Input your Vapor Pressure Data**:

**Set up and try the Polynomial Fit (try 3rd Order):**

Now, you’ll notice that the polynomial coefficients are ‘backwards’ on line 38 above due to the way the coefficient output is put into an array. I haven’t checked but there may be packages in the stats package that calculate the residual difference squared and R squared fit, but I went ahead and put them in for good measure.

**Transform the Data and fit to the Clapeyron Equation:**

Note that the lists have defined as ‘arrays’ and set type to ‘float’. I don’t completely get the Python Types yet, but often I find setting them to float can help.

**Transform the Data and fit to the Antione Equation:
**

Now, it’s ready to run. In the next post, we’ll examine the output and use that to answer the questions.