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The Breusch-Pagan test aims to detect heteroscedasticity in a regression model (the presence of non-constant variance in the error terms). The statsmodels.stats.diagnostic.het_breuschpagan function re …

I agree that this is confusing. Manifest variables and "observed" variables are both observed, in the sense that they are part of the input dataset, and not latent variables. The distinction made in t …

First, the python code for the 1st equation does not implement it correctly. … m=\frac{\sum_{i=0}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=0}^{n}(x_i - \bar{x}^2)}$$ It's $$ m=\frac{\sum_{i=0}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=0}^{n}(x_i - \bar{x})^2} $$ So in (Num)Python …

I am not very experienced with SPSS but If you want to ensure consistency between the two, double-check that Python might handle categorical variables differently in terms of reference categories and dummy … However, to the best of my knowledge, these options are not available in the mixedlm implementation from statsmodels in Python. …

A "singular matrix" error while fitting mixed models can have several causes and there is not enough information given in the question to accurately diagnose the problem in this case. You mention the …

statsmodels and MixedLM in Python do not provide $R^2$ statistics, so you would need to manually extract the variance components from your mixed effects model to compute these values. …

You seem to have quite a large sample size which is probably why the Shapiro-Wilk test returns a small p-value. In general statistical tests for normality are not a great idea in large part for this v …

With a large dataset like yours (20,640 observations), the asymptotic assumptions should hold reasonably well, making the bootstrap and OLS standard errors similar unless there are issues: The bootstr …

You won't have to if you don't split the dataset Is there a python/sklearn implementation of my idea? … It's the type of mistaken thing people do in data science, and Python is used a lot in data science. However, here we try to guide people into a better way of doing things. …

LASSO in Python With Python, in my experience, the most common implementation of LASSO (Least Absolute Shrinkage and Selection Operator) is provided by the scikit-learn library. … For Python, scikit-learn's LASSO is the most straightforward and commonly used implementation, with a solid and efficient coordinate descent algorithm. …

Here's one way to do this in Python: 1. … The pygam library in Python can do this. Constrained Optimisation Models: These can enforce bounds directly during the training process. The scipy.optimize library can do this. …

A zip code would usually be treated as categorical, since there is (presumably) no meaning to the actual value and difference between the numbers, or ordering. The year of building would usually be …

The statsmodels package in python can fit such a model import statsmodels.api as sm import statsmodels.formula.api as smf data = sm.datasets.get_rdataset("dietox", "geepack").data md = smf.mixedlm("Weight …

One use case I can think of is multivariate regression,as opposed to multvariable regression, the former being a model with more than 1 outcome variable, whereas the latter being a model with one outc …

There are several of packages in R that can fit them, and since the R ecosystem is much more mature than that for python (and R is designed specifically for statistics whereas python is a general purpose … If you want to stay within python then you can still call R functions from python using the rpy2 python package: https://rpy2.github.io/doc/v2.9.x/html/introduction.html Edit: I really would recommend …