Row 52075

Row ID: 52075 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 52075 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I'm going to mention something that's not been mentioned below.

Coefficients in non-linear models are conditioned on the other coefficients and values for the additional variables. Effects are non-linear so focusing on coefficients is a bad idea. Some people look at odds ratio but if you look at who does that the most: medical clinical trials which are experiments so they are basically blocking other variables or making other variables part of their design (like demographics).

With what you want to do, you can't do this, so I would encourage you to look at probabilities. You can check out Gelman and Hill's book on Hierarchical modeling because their chapter 3 or around that is on logistic regression and MC simulations.

The causal aspects is iffy because models show correlation, but I don't think your manager is talking causal in the proper statistics way, more in the "I want an explanation for this" which is acceptable for models and what the other commenter said -- thinking about confounders, controls, data structure, what is affected what and how and why, etc. not throwing the kitchen sink at your model

FieldValue
text I'm going to mention something that's not been mentioned below. Coefficients in non-linear models are conditioned on the other coefficients and values for the additional variables. Effects are non-linear so focusing on coefficients is a bad idea. Some people look at odds ratio but if you look at who does that the most: medical clinical trials which are experiments so they are basically blocking other variables or making other variables part of their design (like demographics). With what you wa…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-22
username_encoded Z0FBQUFBQm5Lak1URkl4T2x5T2U0S0ZEcHVwUnRzU0x0RWZrNzI1M0t5ck8yVDVLZGZaaTQybTRQRGlKZDVMVDFYWXBRZVJEVXhMZXRuMk1SYU1DX004N18yZzQza3pfeVlaYVQ0UUUxc0lPcHlNTDlkUnQtU1k9
url_encoded Z0FBQUFBQm5Lak9qRTZmaDdMUWlJNHR6aW1DRWtDSFFkeFd4S2VYVmJMVUNXSzllbDN4THBUS3pfNGd4dW1OVFM2QUNfSUNSU2g4ZWVDaGZVVlRBNUI0YlE2cFAzMFBhV0s3dF8tdFoyVkIxXzZCSm1fbTc0dFBET2FUZ043Q1BhOUdaZ0FwZC1WaGh2aWZTYjV4RWJTcVJOdElzaWdoVmZPQ2s2UnpzM19GdmJadkx0a3MyY2JSSG1aSlZpNnMySDN4LTV4QlI2T0VoemVRNWl5N05UYU8tbzBpbmhqckJaQT09

Raw Record

{
  "text": "I'm going to mention something that's not been mentioned below.\n\nCoefficients in non-linear models are conditioned on the other coefficients and values for the additional variables. Effects are non-linear so focusing on coefficients is a bad idea. Some people look at odds ratio but if you look at who does that the most: medical clinical trials which are experiments so they are basically blocking other variables or making other variables part of their design (like demographics).\n\nWith what you want to do, you can't do this, so I would encourage you to look at probabilities. You can check out Gelman and Hill's book on Hierarchical modeling because their chapter 3 or around that is on logistic regression and MC simulations.\n\nThe causal aspects is iffy because models show correlation, but I don't think your manager is talking causal in the proper statistics way, more in the \"I want an explanation for this\" which is acceptable for models and what the other commenter said -- thinking about confounders, controls, data structure, what is affected what and how and why, etc. not throwing the kitchen sink at your model",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1URkl4T2x5T2U0S0ZEcHVwUnRzU0x0RWZrNzI1M0t5ck8yVDVLZGZaaTQybTRQRGlKZDVMVDFYWXBRZVJEVXhMZXRuMk1SYU1DX004N18yZzQza3pfeVlaYVQ0UUUxc0lPcHlNTDlkUnQtU1k9",
  "url_encoded": "Z0FBQUFBQm5Lak9qRTZmaDdMUWlJNHR6aW1DRWtDSFFkeFd4S2VYVmJMVUNXSzllbDN4THBUS3pfNGd4dW1OVFM2QUNfSUNSU2g4ZWVDaGZVVlRBNUI0YlE2cFAzMFBhV0s3dF8tdFoyVkIxXzZCSm1fbTc0dFBET2FUZ043Q1BhOUdaZ0FwZC1WaGh2aWZTYjV4RWJTcVJOdElzaWdoVmZPQ2s2UnpzM19GdmJadkx0a3MyY2JSSG1aSlZpNnMySDN4LTV4QlI2T0VoemVRNWl5N05UYU8tbzBpbmhqckJaQT09"
}
Explore Dataset Explore Row

Entry Information