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 …

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 …

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."

Nov 14, 2010 · 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 …

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 …

Apr 27, 2022 · The second formula is used with linear regression corrects for the number of degrees of freedom. Notice that the second formula would not make sense in every context. …

Reading about methods and results of statistical analysis, especially in epidemiology, I very often hear about adjustment or controlling of the models. How would you explain, to a non …

Apr 15, 2016 · In its core, linear regression amounts to orthogonal projection of y y on (onto) X X, where y y is the n n -dimensional vector of observations of the dependent variable and X X is …

I am having some difficulty attempting to interpret an interaction between two categorical/dummy variables. For example, lets say there is an interaction term between an individual's gender …

Jun 7, 2020 · I will cite an educational reference to indicate the possible drawbacks. To quote for the case of a Simple Linear Regression Model: Objective: model the expected value of a …