Timeline for Interpretation of removed continuous variables in regression due to linear dependence
Current License: CC BY-SA 4.0
16 events
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| May 20, 2019 at 17:19 | comment | added | whuber♦ | I suspect you might be able to find answers to your question in existing threads by searching on "composition." Here's the first I found: stats.stackexchange.com/questions/68944. | |
| May 20, 2019 at 17:10 | comment | added | Jurgis Samaitis | I see, thank you very much for your answer - it makes much sense. I also haven't heard the term compositional data before, I will take a deeper look into it! Could you post your comment as an answer so I could accept it? Thanks again! | |
| May 20, 2019 at 16:40 | comment | added | whuber♦ | I see. I have been confused by an answer that is completely off the mark (which is no fault of yours). What you have is compositional data: proportions of a whole. But since the last variable, as you note, is completely determined by the first four variables, it makes no sense to ask what its effect is, because you cannot attribute separate effects to any of those variables. That's the basic problem of collinearity: it is impossible to keep all the other variables constant while changing just one of them. | |
| May 20, 2019 at 16:35 | history | edited | Jurgis Samaitis | CC BY-SA 4.0 |
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| May 20, 2019 at 16:34 | comment | added | Jurgis Samaitis | Sorry for the confusion. These variables are the percentage of the population in a particular neighborhood, belonging to an age group. For example Neighborhood Age 0-14 value of 23 would mean that there is 23% of people in a neighborhood, who are between 0-14 years old. | |
| May 20, 2019 at 16:31 | comment | added | whuber♦ | Could you please be more specific about how you have created these variables? Your question currently reads as if they are categories of percentages rather than the percentages themselves. | |
| May 20, 2019 at 16:27 | history | edited | Jurgis Samaitis | CC BY-SA 4.0 |
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| May 20, 2019 at 16:26 | comment | added | Jurgis Samaitis | Thank you for your answer - I understand the interpretation regarding the categorical variables, but these are continuous variables, ranging 0 - 100. Is the interpretation similar to one with categorical variables? | |
| May 20, 2019 at 16:23 | comment | added | whuber♦ | Thank you--that clarifies your question. You might be able to find the answer yourself by comparing these estimates to the original set of estimates. In particular (depending on how your software codes these categorical variables), the intercept should equal the original estimate for the >64% category. | |
| May 20, 2019 at 16:18 | history | edited | Jurgis Samaitis | CC BY-SA 4.0 |
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| May 20, 2019 at 16:18 | comment | added | Jurgis Samaitis | My bad, edited the original question. There were a lot of variables and I tried to keep the confusion to a minimum. | |
| May 20, 2019 at 15:38 | comment | added | whuber♦ | It looks like you did not report the full results: what happened to the intercept? | |
| May 20, 2019 at 15:13 | history | edited | Jurgis Samaitis | CC BY-SA 4.0 |
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| May 20, 2019 at 13:25 | answer | added | Kane Chua | timeline score: -1 | |
| May 20, 2019 at 9:50 | review | First posts | |||
| May 20, 2019 at 10:45 | |||||
| May 20, 2019 at 9:45 | history | asked | Jurgis Samaitis | CC BY-SA 4.0 |