Timeline for "Constant variance" violation
Current License: CC BY-SA 3.0
6 events
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Mar 14, 2013 at 17:37 | comment | added | Penguin_Knight | @Mukul Yes, correct. One line is for the outcome = 0 and the other one is outcome = 1. This is, however, only true if you have one predictor. As you add more predictors, the picture can change to either smearing out (with continuous predictor) or having many straight lines (with categorical predictor.) As for the red arrows, yes you're correct. They show the range of the error, which is not variance. Please consider that as a simplified representation. | |
Mar 14, 2013 at 17:37 | comment | added | Penguin_Knight | @Mukul For me, this would serve just to understand the concept. In real life, I have never segregated the data and check the variance chunk by chunk (too many tests, and the cutting of the sample can also be controversial.) You can simply plot residual$^2$ against the predicted value to observe if there are any violation of constant variance. | |
Mar 14, 2013 at 16:11 | comment | added | Mukul | can you explain why do we have two distinct lines above. Is it because one line corresponds to the residuals calculated for 1's and the other is for 0's. And also as per your last comment. The height of the Red arrows (previous post) is a proxy for varaince. Right ? | |
Mar 14, 2013 at 16:01 | comment | added | Mukul | you just +1 your count of number of your fans. This is the precise answer I was looking for. So this exactly in line with your previous answer to my old question. So we check for constant variance within subsamples of the data by considering the segments(roughly) as mentioned above in your red fonts. That is how we look for constant variance. Hope my understanding is correct | |
Mar 14, 2013 at 15:53 | vote | accept | Mukul | ||
Mar 14, 2013 at 13:26 | history | answered | Penguin_Knight | CC BY-SA 3.0 |