Timeline for In a regression is the Y-intercept a measure of unaccounted biases?
Current License: CC BY-SA 3.0
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| Jun 17, 2020 at 10:55 | vote | accept | Sorade | ||
| Sep 22, 2017 at 13:53 | comment | added | Tim | @Sorade The answer to your question in the comment is also "no". First, in most cases it would be impossible to account for ALL the parameters. Second, it could happen that you have similar accuracy metrics for two different models, with different variables. Third, more variables does not mean that your model is going to be "better" (it can however lead to overfitting). In general, it's complicated. You should probably get some handbook on regression modeling if you find this unclear. | |
| Sep 22, 2017 at 13:47 | comment | added | Sorade | Thank you. I still feel like I don't have an answer to my question, but you gave me material to think about. I'll brew on it a bit and see if I can maybe rephrase my question better. Maybe my question should be: " If my model has shown good predictability (say I have a divided my data into a test and train set) does it mean that I have accounted for ALL the important parameters for sure ? " | |
| Sep 22, 2017 at 13:43 | history | edited | Tim | CC BY-SA 3.0 |
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| Sep 22, 2017 at 13:34 | history | edited | Tim | CC BY-SA 3.0 |
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| Sep 22, 2017 at 13:33 | comment | added | Tim | @Sorade please look at the example once again. The models are identical and give identical predictions (that differ by 5), so how could one of them give less information then the another one? | |
| Sep 22, 2017 at 13:31 | comment | added | Tim | @Sorade I said nothing about removing intercept, I linked the other thread for you to learn more about the role of intercept in regression. What I say is that intercept is related to the mean of Y and nothing more, it does not measure "bias" and I gave you example of identical models widh different intercepts that changed just because the mean of Y has changed. | |
| Sep 22, 2017 at 13:24 | comment | added | Sorade | Thank you Tim, if I understood your answer correctly you are explaining why it is not okay to remove the intercept. I agree with that, my question was more whether the model could be criticised for being to general. Okay I get very good match with my data (adj. R2 > 0.97) but, still, maybe that the large intercept value, means that I don't have enough information to provide meaningful answers. Maybe the data only covered, very light statues. If somebody else was to use the regression for another dataset of heavy statues they wouldn't get a good R2. | |
| Sep 22, 2017 at 13:14 | history | answered | Tim | CC BY-SA 3.0 |