Understanding regression guide for supporting characters requires examining multiple perspectives and considerations. regression - What does it mean to regress a variable against another .... 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 on the other.
Another key aspect involves, generally speaking it makes more sense to use correlation rather than regression if there is no causal relationship. regression - When should I use lasso vs ridge? Ridge regression is useful as a general shrinking of all coefficients together.
It is shrinking to reduce the variance and over fitting. It relates to the prior believe that coefficient values shouldn't be too large (and these can become large in fitting when there is collinearity) Lasso is useful as a shrinking of a selection of the coefficients. regression - Converting standardized betas back to original variables .... I have a problem where I need to standardize the variables run the (ridge regression) to calculate the ridge estimates of the betas. I then need to convert these back to the original variables scale.
regression - How to calculate the slope of a line of best fit that .... This kind of regression seems to be much more difficult. I've read several sources, but the calculus for general quantile regression is going over my head. My question is this: How can I calculate the slope of the line of best fit that minimizes L1 error?
Moreover, some constraints on the answer I am looking for: Difference between linear regression and neural network. Some site claims linear regression means the continuous value output. Furthermore, if I have an MLP with hidden layers, and its output is continuous value (ex: house price), then is it called linear regression? Neural Network with linear activation functions ( doesn't matter binary output, continuous output value, hidden layer) Hope you understood my confusion.
Furthermore, how should outliers be dealt with in linear regression analysis .... What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
correlation - What is the difference between linear regression on y .... The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). Another key aspect involves, this suggests that doing a linear regression of y given x or x given y should be the ...
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...
regression - Difference between forecast and prediction ...
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