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You would<most often be better off using a Weibull plot for assessing distribution fit, see Weibull plot to assess goodness of fit. If you rather want/need a formal goodness of fit test, see A goodness of fit test for the Weibull distribution, but the advice at Is normality testing 'essentially useless'? would equally apply for Weibull testing, ...


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There is no good reason not to do such a generalization, which would unite the Weibull and inverse Weibull distributions. So reasons must be historical or accidental. Also see the comments thread.


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I prefer to equivalently view the AFT model in terms of a generalized linear model like logistic regression or Poisson regression. In these models there is no "error term." There is simply a likelihood and a parameterization of the mean that is estimated through maximum likelihood. Most documentation describe an AFT model in terms of a log scale ...


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how can you interpret the intercept of the output of the survreg model other than as the log of the scale parameter of the distribution? Does it tell us anything useful about the data from a biological perspective...? This difficulty in interpreting the intercept is inherent in AFT models, as you note. For symmetric error terms like that in a log-normal ...


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The precision of survival analysis is typically limited by the number of events. A rule of thumb is that you need about 15 events per parameter that you want to fit; you only have 13 events while a Weibull (and many parametric survival models) has 2 parameters. I'm not sure that there are enough events to rule out a Weibull distribution, but there are many ...


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Only you can answer this question correctly. A predicted survival time will be time from whatever you used as the reference time = 0 for your Years value in your model. If you used a reference time = 0 for each item at its install_date, then yes: the prediction will be time elapsed from the install date. If you used some other reference for time = 0, then it ...


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The plot you display is sometimes called a Weibull plot. See, for example, https://www.itl.nist.gov/div898/handbook/eda/section3/weibplot.htm (which shows your exact plot) and also https://en.wikipedia.org/wiki/Weibull_distribution#Weibull_plot While it's not the only sort of plot you might want to do*, it's a reasonably common way of looking at goodness of ...


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