I am trying to replicate the standard errors that Eviews calculates in linear regression models forecast. I have used the classical standard error formula, but it is not the same. After some research I found this formula:
I couldn't figure out how to replicate.
I appreciate if someone could help me.
Thanks!
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I am creating a logistic regression model on MATLAB which will be a binary classifier (outcome is the label 'good' or 'bad'). I have found what appears to be two functions which can fit a logistic model:
fitglm()
fitclinear()
Can someone please help me understand the difference? So far I have been using fitclinear and enjoying it.
Thanks
I have finetuned two resnet101s on the exact same dataset and with similar hyperparameters, one with Matlab and the other with Pytorch, and created GradCAMs with gradients on the same layers. The results are too far. Seems that the implementation in Matlab gives much more accurate results. Any thoughts are really appreciated.
I am estimating a regression in MATLAB long hand.
I've got the standard error and coefficient, is there any quick way to get the p-value in MATLAB?
Any tips or tricks would be appreciated?
Usually p-values are from a statistical test, to check the statistical significance of the result, using t-test for example.
I have a big matrix with dimensions 2.000X98.000 and i want to perform unsupervised clustering on it. My problem is that when i try the clustering with Scipy.cluster.vq.kmeans2, i get an error that says "Matrix is not positive definite", when I try it with scipy.cluster.vq.kmeans it takes hours and hours to calculate and when i try it with sklearn.cluster.KMeans, the computation is fast and presents no errors. I have read the documentation for all the algorithms and i have researched through the internet for answers , but still i cannot understand this kind of difference between the three of them. Could someone explain to me this fundamental difference between them and why the need of a positive definite matrix is necessary only for scipy.kmeans2? Thank you in advance for your time and consideration.
Could anyone please help in providing an example showing how ANCOVA (analysis of covariance) can be done in scipy/statsmodel, with python?
I am not sure if I am asking too much, but a quick search showed me this which is not informative enough for me.
Thanks!
Statsmodels uses the linear model, OLS, to estimate ANOVA. So, having additional continuous regressors as in ANCOVA does not change the analysis.
Here are a few links to the relevant documentation
Anova helper functions and examples for ANCOVA interactions
http://statsmodels.sourceforge.net/devel/examples/generated/example_interactions.html
using formulas to create the design matrix
http://statsmodels.sourceforge.net/devel/example_formulas.html
the core OLS model
http://statsmodels.sourceforge.net/devel/generated/statsmodels.regression.linear_model.OLS.html