Logistic regression assumptions and Box-Tidwell test
Jan 9, 2025 · I'm doing a logistic regression that includes just two numeric independent variables (age and the sum of affected organs) and others categorical variables. When I perform the …
Regression: within and between-subject variable interaction
Mar 7, 2019 · The values for my between subject variables are doubled and each participant was measured twice, which will affect my degrees of freedom, and thus my p values. Is this a valid …
Regression based on rank observations - Cross Validated
Apr 6, 2025 · The coefficients of an OLS regression are just simple descriptive statistics; you can compute them on any data, w/o having to make any assumption whatsoever, just as you could …
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 …
regression - Trying to understand the fitted vs residual plot?
Dec 23, 2016 · A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is …
Support Vector Regression vs. Linear Regression - Cross Validated
Dec 5, 2023 · Linear regression can use the same kernels used in SVR, and SVR can also use the linear kernel. Given only the coefficients from such models, it would be impossible to …
When to use Deming regression - Cross Validated
Apr 19, 2017 · To my understanding, deming regression is suitable if respone (y) and explanatory (x) variable both have (measuring) errors and are interchangeable. Deming regression …
regression - Dealing with bimodal residuals - Cross Validated
Mar 25, 2022 · I want to run linear models to understand the effect of single predictors on an outcome. This is quite straightforward, but I am facing a situation where my residuals appear …
correlation - What is the difference between linear regression on y ...
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 …
Modelling mortality rates using Poisson regression
Dec 14, 2014 · The Poisson regression doesn't care whether the data as aggregated or not, but in practice non-aggregated data is frail and can cause some unexpected errors. Note that you …