Timeline for R-squared and fit of the model
Current License: CC BY-SA 3.0
9 events
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
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| Jan 26, 2016 at 13:14 | comment | added | Bear | Yeah, it is more about defining rules of simulation. The data is time-series, so I've spent some time on ARIMA, exp. smooting modelling with poor results. OLS regression, however, did some improvement to my MAE score. | |
| Jan 26, 2016 at 13:00 | comment | added | Nick Cox | OK. But it's then hardly surprising that regression doesn't find relationships if there aren't any to speak of! | |
| Jan 26, 2016 at 12:57 | comment | added | Bear | That's for my personal knowledge. I don't know how to analize such noisy distribution/data at all. May be you can suggest where to look forward to learn? @Flounderer has defined data pretty well. It is simply fake random generated data according to some rules. I'm trying to learn some techics, which might be helpful with defining that rules. | |
| Jan 26, 2016 at 12:47 | comment | added | Nick Cox | I don't see why not from what you have told us, but we don't know the context of the work, what the variables are, what is known scientifically, who you will be reporting to, etc. | |
| Jan 26, 2016 at 12:35 | comment | added | Bear | Thank you for your time and explanation of this techics. Neglog transformation hasn't helped me, but the cube root does. Once I rescaled all the data by cube root, the regression shown that only B variable significantly different from zero. The neglog transformation hasn't helped me, as I get the same result as with original data. Am I corretly understand this technics? Can I make decision about using regressors by cube root transformation? | |
| Jan 26, 2016 at 12:23 | vote | accept | Bear | ||
| Jan 26, 2016 at 12:14 | history | edited | Nick Cox | CC BY-SA 3.0 |
added 727 characters in body
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| Jan 26, 2016 at 12:07 | history | answered | Nick Cox | CC BY-SA 3.0 |