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We have seen an introduction of logistic regression with a simple example how to predict a student admission to university based on past exam results. This was done using Python, the sigmoid function and the gradient descent. We can now see how to solve the same example using the statsmodels...

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Note that statsmodels, just like sklearn that we will encounter a bit later, uses an object-oriented approach. In this approach you initialize objects that store the data and methods together. This allows for an organized approach to storing and check data and parameters, and applying computations to them, such as fitting models.

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Anyone know of a way to get multiple regression outputs (not multivariate regression, literally multiple regressions) in a table indicating which different independent variables were used and what the coefficients / standard errors were, etc. Essentially, I'm looking for something like outreg, except for python and statsmodels.

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A standard dict will produce effectively random order. Clustering on entity reduced the t-stats across the board. This suggests there is important correlation in the residuals per entity. Other clusters can be used by directly passing integer arrays (1 or 2 columns, or a 1-d array) using the input clusters.

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Clustered standard errors are a special kind of robust standard errors that account for heteroskedasticity across "clusters" of observations (such as states, schools, or individuals). The clustering is performed using the variable specified as the model's fixed effects.