What is the effect of having correlated predictors in a multiple ...
The VIF is how much the variance of your regression coefficient is larger than it would otherwise have been if the variable had been completely uncorrelated with all the other variables in the model. Note …
How to derive variance-covariance matrix of coefficients in linear ...
Aug 24, 2024 · How to derive variance-covariance matrix of coefficients in linear regression Ask Question Asked 12 years, 3 months ago Modified 1 year, 3 months ago
r - Data preparation for regression - Cross Validated
I am trying to predict real estate sales prices. In my dataset there are independent variables that are both nominal and numeric (square meters, prices etc.) Before feeding the data to any regres...
Should we standardize the data while doing Gaussian process …
17 I am performing Gaussian process regression (GPR) and optimizing over hyper-parameters. I am using minFunc to perform all optimizations. My question is should we (or rather, can we) standardize …
Logistic regression with binary dependent and independent variables
Aug 22, 2011 · Is it appropriate to do a logistic regression where both the dependent and independent variables are binary? for example the dependent variable is 0 and 1 and the predictors are contrast …
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)?.
Is it necessary to scale the target value in addition to scaling ...
I'm building regression models. As a preprocessing step, I scale my feature values to have mean 0 and standard deviation 1. Is it necessary to normalize the target values also?
Formula for weighted simple linear regression - Cross Validated
Jul 5, 2011 · This wiki page Simple linear regression has formulas to calculate $\\alpha$ and $\\beta$. Could anyone tell me how to derive the formulas in weighted case?
survival - Prediction in Cox regression - Cross Validated
In a linear or logistic regression, it would be easy, just put the values of new observation in the regression and multiply them with betas and so I have the prediction of my outcome.
machine learning - Why use gradient descent for linear regression, …
May 11, 2017 · The main reason why gradient descent is used for linear regression is the computational complexity: it's computationally cheaper (faster) to find the solution using the gradient descent in …