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When conducting multiple regression, when should you center your ...
In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividin...
In linear regression, when is it appropriate to use the log of an ...
This is because any regression coefficients involving the original variable - whether it is the dependent or the independent variable - will have a percentage point change interpretation.
What is the lasso in regression analysis? - Cross Validated
LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regression.
Support Vector Regression vs. Linear Regression - Cross Validated
Linear regression can use the same kernels used in SVR, and SVR can also use the linear kernel. Given only the coefficients from such models, it would be impossible to distinguish between them in the general case (with SVR, you might get sparse coefficients depending on the penalization, due to $\epsilon$-insensitive loss)
regression - When is R squared negative? - Cross Validated
Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative.
Interpreting interaction terms in logit regression with categorical ...
My own preference, when trying to interpret interactions in logistic regression, is to look at the predicted probabilities for each combination of categorical variables.
How to derive the standard error of linear regression coefficient
another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X) df from Wikipedia: "...In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of parameters used as intermediate steps in the estimation of the parameter itself ."
Linear regression when independent variable are count data
The Ys, on the other hand, are continuous and can assume any numerical value, either positive or negative. Initially, my approach was to apply linear regression to model this relationship. However, given the specific nature of the Xs as count variables, I've grown uncertain about the appropriateness of using linear regression.
How to calculate pseudo-$R^2$ from R's logistic regression?
A somewhat related question was asked here, Logistic Regression: Which pseudo R-squared measure is the one to report (Cox & Snell or Nagelkerke)?.
Why Isotonic Regression for Model Calibration?
1 I think an additional reason why it is so common is the simplicity (and thus reproducibility) of the isotonic regression. If we give the same classification model and data to two different analysts, then each of them might get different recalibrations depending on the regression function they choose and its parameters.
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