UCLA ATS has very good introduction of Applied Statistics, including using R/SAS/Stata to do hands-on projects. Here I am trying to provide a python version of the web book about linear regression. At least I will try to cover their first 3 to 4 Chapters based on my time schedule. I will focus on Chapter 2 to discuss linear regression diagnostic. In Chapter 1 I will introduce how to run linear regression in python statsmodels to get the same result as R or SAS. And how to do data analysis and data visualization in python.In the future, I will try to introudce machine learning in sklearn and deep learning in Theano and Tensorflow.
linear regression in python, outliers / leverage detect
in section I will introduce how to detect ourliers and high leverage points in the linear regression. I also shows in graph how the ourliers will affect your regression fitting. More details of detecting using cook's distance, dffits, dfbeta will be in section 2 -- regression diagnostic.
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