Linear model with log-transformed response vs. generalized linear …
In a generalized linear model, the mean is transformed, by the link function, instead of transforming the response itself. The two methods of transformation can lead to quite different results; for example, …
Linear regression and assumptions about response variable
9 Wikipedia states: Ordinary linear regression predicts the expected value of a given unknown quantity (the response variable, a random variable) as a linear combination of a set of observed values …
In linear regression, when is it appropriate to use the log of an ...
Aug 24, 2021 · If you log the independent variable x to base b, you can interpret the regression coefficient (and CI) as the change in the dependent variable y per b -fold increase in x. (Logs to base …
Residuals correlated positively with response variable strongly in ...
Apr 18, 2012 · In linear models, the explanatory variables are usually assumed to be non-random, so talking about their distribution (or, indeed, correlations) is meaningless. Are you sure that multiple …
How to fit a mixed model with response variable between 0 and 1?
Sep 5, 2016 · 21 I am trying to use lme4::glmer() to fit a binomial generalized mixed model (GLMM) with dependent variable that is not binary, but a continuous variable between zero and one. One can think …
generalized linear model - modeling response variable that is ...
Apr 16, 2020 · 3 I am attempting to model data where the response variable y is a proportion (ratio of counts=successes / (successes+failures) in the range (0,1) the predictors are also proportions …
In linear regression why does the response variable have to be ...
Mar 12, 2017 · 15 I know that in linear regression the response variable must be continuous but why is this so? I cannot seem to find anything online that explains why I cannot use discrete data for the …
A regression in R with a categorical response variable
Mar 4, 2022 · I have a dataset with a categorical response variable (integers from 1 till 10) and numerical predictors (independent variables). I want to be able to predict the value (rank, perhaps) …
How to handle skewed data and response variable when predicting
My data contains some skewed features, and also the response variable (sale price) is also skewed. Log transforming all relevant features and the response variable is good enough and 'fixes' the s...
Regression with heavy-tailed response variable - Cross Validated
Feb 25, 2020 · So I am hoping to use this variable as the dependent variable in a regression model, with a mixture of continuous and categorical predictor variables.