I\'d like to do large-scale regression (linear/logistic) in R with many (e.g. 100k) features, where each example is relatively sparse in the feature space---e.g., ~1k non-zero features per example.
I hope that this one is not going to be \"ask-and-answer\" question... here goes: (multi)collinearity refers to extremely high correlations between predictors in the regression model. How to cure them
there are 3 regression bugs while doing a regression test for a software. \"local\",\"unmasked\" and \"remote\". Does any one kn开发者_运维技巧ow the definition of each?
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I开发者_如何学Gos it possible to do regressions in R using a panel data set with a binary dependent variable? I am familiar with using glm for logit and probit and plm for panel data, but am not sure
I am now looking at a panel dataset on which I have to regress. Since I only started my Phd this semester together with the econometrics courses I am still new to many statistic applications and regre
I see that it is possible to use regress/regstats for OLS, and I found an online implementation of L1-Regression (Laplace), but I can\'t quite seem to figure out how to implement t distributed error t
I am trying to do a multivarible (9 variables) linear regression on data in my mysql 5.0 database (the result value field only has 2 开发者_JS百科possible values, 1 and 0).
I have a weird situation with scipy.stats.linregress seems to be returning an incorrect standard error:
How to make a linear regression on the char开发者_如何学Pythont displayed into your BIRT report.
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