I'm trying to fit a multiple linear regression model to my data with couple of input parameters, say 3.

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 was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."

Dec 4, 2014 · When we say, to regress Y Y against X X, do we mean that X X is the independent variable and Y the dependent variable? i.e. Y = aX + b Y = a X + b.

With linear regression with no constraints, R2 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r. A negative R2 R 2 is only possible with linear regression when either …

Sample size calculation for logistic regression is a complex problem, but based on the work of Peduzzi et al. (1996) the following guideline for a minimum number of cases to include in your study can be …

I am trying to perform a multiple regression in R. However, my dependent variable has the following plot: Here is a scatterplot matrix with all my variables (WAR is the dependent variable): I know ...

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 ...

Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the model for all points combined can't be "correct" if the …

Another beauty of this way of looking at things is how it can help us understand ridge regression. When we want to really understand regression, it almost always helps to think of it geometrically: the …