Timeline for Small-sample binary logit and linear models - response to referees
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Feb 28 at 8:35 | comment | added | CaroZ | What is it you call "time occurrence" ? For the mathematical reason, see Frank Harrell's answer above. I can only tell you an answer based on common sense : if something is present almost everywhere, you do not have enough cases of non-occurrence to estimate the probability of non-occurrence. It would be a case of really wanting to fit an analysis in somewhere where a simple description of what you observe would be accurate. | |
| Feb 27 at 12:13 | comment | added | zhiheng yi | Because I am a beginner in statistics, and my methods are based on referencing papers from similar researchers, so there may be many errors. I am curious, is it unreasonable to analyze the presence of MP based on the assumption of a high probability of time occurrence (80%)? What are the specific reasons for this? | |
| Feb 27 at 12:13 | history | edited | CaroZ | CC BY-SA 4.0 |
added 1 character in body
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| Feb 27 at 12:05 | history | edited | CaroZ | CC BY-SA 4.0 |
added 1 character in body
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| Feb 27 at 12:04 | comment | added | CaroZ | So then why do you want to analyse it as presence/absence when it is obviously almost always present ? | |
| Feb 27 at 11:59 | comment | added | zhiheng yi | The concentration of MP is also one of the outcome variables, which is a continuous variable. Therefore, we have already used a multiple linear regression model for statistical analysis. | |
| Feb 27 at 11:58 | history | edited | CaroZ | CC BY-SA 4.0 |
Made it an answer instead of a comment
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| Feb 27 at 11:51 | comment | added | Stephan Kolassa | This sounds more like a comment than an answer. | |
| Feb 27 at 11:50 | history | answered | CaroZ | CC BY-SA 4.0 |