This chapter will cover the linear regression with categorical variables: how to create dummy variables, how to run the categorical variables regression directly, what does the categorical mean, and what does it mean when there is interactrion, especially when there is interactions with continuous variables. For the original document, plrease refer to UCLA ATS

Happy New Year

This chapter will cover the diagnostics of linear regression. For the original document, plrease refer to UCLA ATS

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.

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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this is a notes about how to delply flask web pages on digitalocean

How to install a package in sublime text

show how to plot on the web with highcharts

show how to let a column in the data be the tick in the x axis