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Jun 1, 2021 at 11:45 history edited mkt CC BY-SA 4.0
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Mar 4, 2021 at 6:18 history edited mkt CC BY-SA 4.0
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May 17, 2019 at 9:21 history edited mkt CC BY-SA 4.0
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May 13, 2019 at 13:54 history edited mkt CC BY-SA 4.0
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May 13, 2019 at 11:58 vote accept Stevestingray
May 13, 2019 at 11:54 comment added mkt @TomvanHeusden How much that is really depends on the values in your dataset, since the standard deviation is calculated from the data; this is unlike unstandardized coefficients which are more easy to apply outside the context of your data. But you can also transform the values back onto the original scale.
May 13, 2019 at 11:53 comment added mkt @TomvanHeusden No problem, I should have been clearer: instead of change, I should have said increase. I've edited the answer to correct that.
May 13, 2019 at 11:51 history edited mkt CC BY-SA 4.0
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May 13, 2019 at 11:32 comment added Stevestingray Thanks a lot for your explanation! One last question if thats OK: I can't really wrap my head around the interpretation. I know what it means now, but i can't imagine what the effect is in terms of standard deviations. If i understand correctly: A one standard deviation change is not an in- or decrease in the value of the predictor? How does a 'one standard deviation' relate to the original values? What does it mean ín the field?'. Sorry if it's is a simple question, but i cant figure it out.
May 13, 2019 at 10:37 history edited mkt CC BY-SA 4.0
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May 13, 2019 at 10:34 comment added mkt @TomvanHeusden No need to do that! I've edited my answer to explain things based on your comment here.
May 13, 2019 at 10:28 comment added Stevestingray Thanks so much for the explanation! I defined my model better in the top. Unfortunately, I did not normalize the dependant variable (vegetation change). I will run a new model with the normalized vegetation scores.
May 13, 2019 at 9:39 history edited mkt CC BY-SA 4.0
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May 13, 2019 at 9:32 history answered mkt CC BY-SA 4.0