Timeline for Statistical models for Obesity data
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 27, 2015 at 20:10 | comment | added | snoram | Be aware of possible confounding variables when interpreting the model, you are unlikely to pinpoint exact "determinants" with the data you have. | |
| Mar 27, 2015 at 17:39 | comment | added | Jeremy Miles | SEM is great at what it does, but I don't see what you need here that doesn't involved logistic regression. | |
| Mar 27, 2015 at 16:55 | comment | added | JRK | I am also using linear regression model by considering BMI (continuous variable) as response variable | |
| Mar 27, 2015 at 16:52 | comment | added | EdM | Is there some reason why you want to code obese versus non-obese when you have actual BMI values? Much information can be lost when you dichotomize a continuous variable like BMI. You could simply have a linear regression of BMI (or perhaps some continuous transformation of BMI) against the predictor variables. | |
| Mar 27, 2015 at 16:49 | vote | accept | JRK | ||
| Mar 27, 2015 at 16:45 | answer | added | rnso | timeline score: 3 | |
| Mar 27, 2015 at 15:18 | history | asked | JRK | CC BY-SA 3.0 |