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Results tagged with regression
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user 81019
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
2
votes
2
answers
465
views
Choosing to keep or bin continuous variables for communicating regression results
I am using logistic regression in a work setting (e.g. subscription conversion in a technology product). … The independent variables of this regression include both continuous and categorical variables. …
- 797
1
vote
0
answers
41
views
For Logistic Regression, should I avoid changing discrete independent variable to binary ind... [closed]
I am running multiple logistic regression to understand feature importance. … I wouldn't want my coefficient from the regression to just be noise, which I anticipate the non-bucketed distribution would be susceptible to. Maybe I am misunderstand logistic regression though. …
- 797
1
vote
1
answer
90
views
Am I interpreting logistic regression coefficient of categorical variable correctly as a pro...
Since this is easy to get wrong, I wanted to spot check my line of thinking:
I have a logistic regression $Y = \beta_0 + \beta_1 X$, where $Y$ is a binary response and $X$ is a categorical variable with … EDIT: Increases in probability relative to the level of the categorical variable left out in the regression, so to speak. …
- 797
2
votes
1
answer
47
views
Intuitively, why is it possible to have a linear regression with significant predictors that...
Just wondering -- I just ran a regression where my $R^{2}$ value was 0.046 but I have four independent variables with p-values below 0.05. …
- 797
11
votes
2
answers
2k
views
Does a positive interaction term imply correlation between its constituent variables?
Let's say I'm running a linear regression that has the form $y = \beta_0 + \beta_1A+\beta_2B+\beta_3AB +\epsilon$. …
- 797
1
vote
0
answers
534
views
Using gbm to eliminate variables before glm
I have a classification problem I am attempting to model using logistic regression (via the glm package in R):
cols <- c("x", "z", "a", "b", "c")
formula = paste0("x ~ ", paste(cols, collapse = "+") … How advisable is it to model this relationship using gbm, see the relative inference strength of each variable, and then remove seemingly meaningless variables before glm regression? …
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