Dec 5, 2023 · 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 …

Dec 21, 2016 · Those words connote causality, but regression can work the other way round too (use Y to predict X). The independent/dependent variable language merely specifies how one thing depends …

Apr 15, 2016 · The word "regressed" is used instead of "dependent" because we want to emphasise that we are using a regression technique to represent this dependency between x and y. So, this …

Apr 21, 2025 · The Journal of Multivariate Analysis specifies that regression using multiple features to predict one outcome is outside of their scope. Submissions dealing with univariate models, including …

Dec 9, 2023 · If a population model has k independent variables and 1 intercept, why are k+1 observations required to perform OLS estimates? What is the intuition behind this?

The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the ...

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 …

Oct 19, 2011 · 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 …

I have run a simple linear regression of the natural log of 2 variables to determine if they correlate. My output is this: R^2 = 0.0893 slope = 0.851 p < 0.001 I am confused. Looking at the $...

Mar 22, 2023 · When running a multiple regression analysis, why do we not need to correct the p-values for the amount of predictors in the model? summary(lm(mpg ~ disp + hp + drat + wt + gear, …