Timeline for Data Transformation to achieve Linearity
Current License: CC BY-SA 4.0
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| Dec 20, 2022 at 18:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 21, 2022 at 22:05 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Apr 17, 2022 at 14:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Dec 17, 2021 at 10:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Feb 13, 2021 at 6:00 | history | tweeted | twitter.com/StackStats/status/1360468765813727233 | ||
| Feb 20, 2020 at 18:12 | answer | added | kjetil b halvorsen♦ | timeline score: 1 | |
| Feb 20, 2020 at 17:59 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
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| Dec 17, 2018 at 9:08 | comment | added | user2974951 | What about WLS? Modelling the variance looks to be necessary, since the residual variance does decrease to the right. | |
| Dec 16, 2018 at 20:24 | comment | added | James Phillips | If you post a link to the data, I can perform an equation search using my zunzun.com open source web site's "function finder". It uses a genetic algorithm to find initial parameter estimates for non-linear equations, allowing the site to search through large numbers of both linear and non-linear equations - it has hundreds of known, named equations to search through. | |
| Dec 16, 2018 at 18:09 | comment | added | Hans Meier Ruth | Thanks @Stefan The 'probem' is that it is a study for the university and a prerequisite is to use OLS/ WLS regression. All other variables look fine, this is he only problematic variable. The variable on the x-axis is the duration of the treatment in days. | |
| Dec 16, 2018 at 17:55 | comment | added | Stefan | You don't necessarily need to transform your data if the linearity assumption doesn't hold. Instead you could try a different distribution that better describes that data and perhaps improve the residual plot pattern - but you probably have done this already. What's the nature of your dependent variable? Have you tried a GAM (General Additive modeling) approach yet? This might be helpful: stats.stackexchange.com/questions/280344/… | |
| Dec 16, 2018 at 17:46 | history | edited | Hans Meier Ruth | CC BY-SA 4.0 |
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| Dec 16, 2018 at 17:12 | history | edited | Hans Meier Ruth | CC BY-SA 4.0 |
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| Dec 16, 2018 at 16:56 | history | asked | Hans Meier Ruth | CC BY-SA 4.0 |