Topics covered: Covariance, correlation, and chi-square fitting.
In the second Jupyter notebook we will be using the output of some data analysis done using data from the Fermi Gamma-ray space Telescope. It is the same data we used last week; except this time we will be looking for a long-term trend in the Vela pulsar, to see if it is slowly getting brighter or fainter.
We will not be using a lot of new python functions this week. Here are the important ones that we will be using.
| Function Name | What it does |
|---|---|
| plt.figure | Make a matplotlib figure, useful for making figures with subplots |
| fig.subplots | Makes subplots for a figure |
| np.cov | Compute the covariance matrix between multiple data series |
| np.corrcoef | Compute the correlation coefficient between multiple data series |
| plt.contour | Make a contour plot, ie., show the contours correspond to a series of values |
| scipy.stats.minimize | Find the parameter values that minimize a user-provided “cost function” |
| scipy.stats.chi2 | Interact with a $\chi^2$ distribution, e.g., to compute a p-value |