Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't …

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.

Feb 2, 2020 · One outcome, one explanatory variable, often used as the introductory example in a first course on regression models. multivariate multivariable regression. Multiple outcomes, …

Consider the following figure from Faraway's Linear Models with R (2005, p. 59). The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a

Consequently, the solution of the Normal equations will immediately become possible and it will rapidly become numerically stable as $\nu$ increases from $0$. This description of the …

Nov 8, 2018 · Some site claims linear regression means the continuous value output. If I have an MLP with hidden layers, and its output is continuous value (ex: house price), then is it called …

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 …

Jun 5, 2012 · 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 …

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 …

Jan 6, 2021 · Three linear machine learning algorithms: Linear Regression, Logistic Regression and Linear Discriminant Analysis. Five nonlinear algorithms: Classification and Regression …