In this post I will use Python to explore more measures of fit for linear regression. I will consider the coefficient of determination (R2), hypothesis tests (, , Omnibus), AIC, BIC, and other measures. This will be an expansion of a previous post where I discussed how to assess linear models in R, via the IPython notebook, by looking at the residual, and several measures involving the leverage.
This is the first in a series of posts using the small data sets from The Handbook of Small Data Sets to illustrate introductory techniques in text processing, plotting, statistics, etc. The data sets are collected in a ZIP file at publisher’s website in the link above. Someone decided to format the data files to resemble the published format to the greatest degree possible, which makes parsing the files interesting. First, we will import our modules,
In this post I will look at several techniques for assessing linear models in R, via the IPython Notebook interface. I find the notebook interface to be more convenient for development and debugging because it allows one to evaluate cells instead of going back and forth between a script and a terminal.
A moving average takes a noisy time series and replaces each value with the average value of a neighborhood about the given value. This neighborhood may consist of purely historical data, or it may be centered about the given value. Furthermore, the values in the neighborhood may be weighted using different sets of weights. Here is an example of an equally weighted three point moving average, using historical data,