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Plot of resid*Flux1. Legend: A = 1 obs, B = 2 obs, etc.
resid |
|
0.4 +
|
|
|
| A
|
0.3 +
|
|
|
|
| A
0.2 + A
|
| A
| A
| A
| A
0.1 + A A
| A A A A A A A A
| A BA
| BA A A A A B A
| A A A BA AAB A A A
| AAAA A A B AA AA A A
0.0 + AC B A AAA A A
| AA A AA A A A
| DA A B A A A B
| A A B A A A A AA A A
| A
| A A A A A A
-0.1 + A A
| A
| A AA A
|
| A
|
-0.2 + A A A
|
|
|
| A
|
-0.3 +
|
---+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
Flux1
here is my resid vs fitted plot, i was not sure it seems ok, could you help me? what should do next, if I face this type of plot.
Accepted Solutions
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What you're looking for:
- The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is reasonable.
- The residuals roughly form a "horizontal band" around the 0 line. This suggests that the variances of the error terms are equal.
- No one residual "stands out" from the basic random pattern of residuals. This suggests that there are no outliers.
Source: https://onlinecourses.science.psu.edu/stat501/node/277/
What you have:
- Residuals that are not bouncing randomly. They are lower around the lower values of flux and more spread out towards the higher end.
- No horizontal band, it's more of a funnel shape.
- There are several residuals that seem problematic, one high up and one low down.
Next steps:
- Attempt to refit the model to see if another variable reduces the error.
- Transformation on variables to reduce variance
- Something else, that makes sense given the contact of your problem which we only know a few things about.
@smorkoc wrote:
Plot of resid*Flux1. Legend: A = 1 obs, B = 2 obs, etc.
resid |
|
0.4 +
|
|
|
| A
|
0.3 +
|
|
|
|
| A
0.2 + A
|
| A
| A
| A
| A
0.1 + A A
| A A A A A A A A
| A BA
| BA A A A A B A
| A A A BA AAB A A A
| AAAA A A B AA AA A A
0.0 + AC B A AAA A A
| AA A AA A A A
| DA A B A A A B
| A A B A A A A AA A A
| A
| A A A A A A
-0.1 + A A
| A
| A AA A
|
| A
|
-0.2 + A A A
|
|
|
| A
|
-0.3 +
|
---+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
Flux1here is my resid vs fitted plot, i was not sure it seems ok, could you help me? what should do next, if I face this type of plot.
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What you're looking for:
- The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is reasonable.
- The residuals roughly form a "horizontal band" around the 0 line. This suggests that the variances of the error terms are equal.
- No one residual "stands out" from the basic random pattern of residuals. This suggests that there are no outliers.
Source: https://onlinecourses.science.psu.edu/stat501/node/277/
What you have:
- Residuals that are not bouncing randomly. They are lower around the lower values of flux and more spread out towards the higher end.
- No horizontal band, it's more of a funnel shape.
- There are several residuals that seem problematic, one high up and one low down.
Next steps:
- Attempt to refit the model to see if another variable reduces the error.
- Transformation on variables to reduce variance
- Something else, that makes sense given the contact of your problem which we only know a few things about.
@smorkoc wrote:
Plot of resid*Flux1. Legend: A = 1 obs, B = 2 obs, etc.
resid |
|
0.4 +
|
|
|
| A
|
0.3 +
|
|
|
|
| A
0.2 + A
|
| A
| A
| A
| A
0.1 + A A
| A A A A A A A A
| A BA
| BA A A A A B A
| A A A BA AAB A A A
| AAAA A A B AA AA A A
0.0 + AC B A AAA A A
| AA A AA A A A
| DA A B A A A B
| A A B A A A A AA A A
| A
| A A A A A A
-0.1 + A A
| A
| A AA A
|
| A
|
-0.2 + A A A
|
|
|
| A
|
-0.3 +
|
---+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
Flux1here is my resid vs fitted plot, i was not sure it seems ok, could you help me? what should do next, if I face this type of plot.
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Thanks for the help. I will consider transformations for the variables.
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Is flux1 truly the fitted value (which can also be called the predicted value), or is it actually the X value used in the regression?
Paige Miller
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It is fitted value (predicted value).