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 8, 2018 · Linear regression works from mathmatical formula through taking data points (inputs) and finding a formula (using formulae) - coefficients, weights, to fit a data model.

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 …

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 ...

Finally, logistic regression only works for data that is 0-1-valued (TRUE-FALSE-valued), like "has a disease" versus "doesn't have the disease". Thus, the Poisson distribution makes the most sense for …

Jun 30, 2023 · Explicitly, a logistic regression does no classification, instead returning predicted probabilities of event occurrence. However, the machine learning terminology seems to refer to …

What is the main difference between maximum likelihood estimation (MLE) vs. least squares estimaton (LSE) ? Why can't we use MLE for predicting $y$ values in linear ...

Sep 2, 2015 · What is the problem? What do you want to do? Will the model be used for prediction in the future, and avoid measuring those 8 variables will save money? If not, I cannot see any problem with …

I see a similar constrained regression here: Constrained linear regression through a specified point but my requirement is slightly different. I need the coefficients to add up to 1. Specifically...

I am a biology student. We do many Enzyme Linked Immunosorbent Assay (ELISA) experiments and Bradford detection. A 4-parametric logistic regression (reference) is often used for regression these …