regression - Why do we say the outcome variable "is regressed …
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 …
regression - What does it mean to regress a variable against …
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 …
How to choose reference category of predictors in logistic …
Feb 1, 2024 · I am struggling to decide which reference category I should define in my logistic regression model. When I define "mandatory school" as a reference in the variable …
What is the lasso in regression analysis? - Cross Validated
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 …
How to adjust regression models for selection bias?
Feb 20, 2025 · I am trying to better understand how regression models can be made to correct against different biases within the data. For example, consider the following situation: I have a …
regression - When is R squared negative? - Cross Validated
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 …
Simple linear regression output interpretation - Cross Validated
I have run a simple linear regression of the natural log of 2 variables to determine if they correlate. My output is this: R^2 = 0.0893 slope = 0.851 p < 0.001 I am confused. Looking at the $...
Why not approach classification through regression?
86 "..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of …
Why Isotonic Regression for Model Calibration?
Jan 27, 2025 · 1 I think an additional reason why it is so common is the simplicity (and thus reproducibility) of the isotonic regression. If we give the same classification model and data to …
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?