Timeline for Should I normalize my continuous predictors prior to the logistic regression
Current License: CC BY-SA 4.0
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| when toggle format | what | by | license | comment | |
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| Nov 23 at 20:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 19 at 16:36 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
added 18 characters in body
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| Oct 19 at 16:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jun 19 at 11:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Dec 7, 2021 at 19:17 | comment | added | R Beginner | @Noah So as long as I use a flexible model (spline or GAM), then the assumption of logistic regression will always be met? | |
| Dec 7, 2021 at 10:34 | answer | added | cdalitz | timeline score: 0 | |
| Dec 7, 2021 at 6:31 | comment | added | Noah | No; the plot will by definition show an exactly linear fit. It doesn't tell you anything. Instead of assessing linearity, why don't you just fit a flexible model? | |
| Dec 7, 2021 at 5:21 | comment | added | R Beginner | So, am I checking the assumption (linearity between logit outcome vs predictor) correctly? | |
| Dec 7, 2021 at 5:06 | comment | added | Noah | What makes you think predictors have to be normally distributed? That is not an assumption of logistic regression. | |
| Dec 7, 2021 at 3:33 | history | asked | R Beginner | CC BY-SA 4.0 |