# DocBuckets

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bio website mic.wustl.edu location St. Louis, MO age member for 1 year, 10 months seen Aug 6 at 15:41 profile views 38

Intermediate statistics, mostly self taught so don't take me too seriously.

 Jun11 comment Regression for an outcome (ratio) between 0 and 1 Back when I wrote this comment, I used REGRESSION after log-transforming the data. Since then I've written a more sophisticated version that uses GLM. I deal with light emission measurements and my testing suggested gamma regression with a log-link was the least prone to runaway uncertainty on the parameters. For most of my real data, the answers from using normal, negative-binomial, and gamma with log-link were all really similar (at least to the precision I needed) May29 comment How to code separate data for a regression parameter in nonlinear regression? Alright you got me, I was rushing to think of an example between lab work and got sloppy... pretend I had an example on hand that didn't simplify. I'm asking about a theoretical statistical method, not about a specific equation. May29 comment How to code separate data for a regression parameter in nonlinear regression? I used that equation because I'm familiar with it, not because it's a specific equation giving me trouble. I've updated my question with a less convenient example. May23 comment Combining two confidence intervals/point estimates If going back to confidence intervals from the pooled SE, what would the degrees of freedom for the T distribution be? Would this change if combining more than 2 confidence intervals? May20 comment Confidence error bars and “central point”: Should we emphasize the median? @whuber After I wrote the edit, I realized that I was looking at it all wrong. Indeed I got away from the definition of the CI and was confusing the distribution of the parameter and the distribution of the repeated estimate of that parameter. Thanks for setting me back on track. May20 comment Confidence error bars and “central point”: Should we emphasize the median? @whuber: I realized that a few minutes after I submitted the edit. I think it clicked. May20 comment Confidence error bars and “central point”: Should we emphasize the median? I did my best to clarify in the body of the question. @whuber: confidence limits are defined much more like the median than the mean, which is why I bring median into the discussion in the first place. As confidence goes to 0, the confidence interval goes to a median value (which is also the mean, when the distribution is symmetrical e.g. normal). Perhaps I am making a mistake in trying to push concepts easily understood in symmetrical distributions into asymmetric ones. May18 comment What does the Scale parameter mean in linear regression? Does this apply to models that don't use probit or logit? My example specifically uses two continuous variables. Does scale still just account for heteroscedasticity? May15 comment What are common statistical sins? I try to be statistically literate and still fall for this one from time to time. What are the alternatives? Change your model so the old null becomes $H_1$? The only other option I can think of is power your study enough that a failure to reject the null is in practice close enough to confirming the null. E.g. if you want to make sure that adding a reagent to your cells won't kill off more than 2% of them, power to a satisfactory false negative rate. May15 comment Nonlinear regression: Confidence intervals on transformed or untransformed parameters? Oh, ok. So this answer is applicable in this case but not necessarily in the generalized case. Got it. On reading up more on the lognormal distribution, it seems that even method 2 isn't the best way to do it, but it's a lot better than method 1. That's answer enough for me- thanks for your help. May14 comment Nonlinear regression: Confidence intervals on transformed or untransformed parameters? By symmetrical, you mean the variance of $y$ is symmetrical around the mean of $y$, correct? In the generalized case where this is true, is the uncertainty of each parameter also symmetrical around the prediction of that parameter? If that's true, then what you say makes perfect sense. Nov12 comment How do I do nonlinear generalized estimating equations in SPSS If I am reading various online resources correctly, difference-in-difference is just another term for multilevel regression. I know that I can do this with SPSS's Generalized Linear Regression procedure (or even a carefully planned non-linear regression in certain circumstances) but these aren't the same as GEE unless I am doing linear regression (which I am not). Jun6 comment Degrees of freedom in quadratic regression simultaneous confidence bands oh ok, I will do some further reading as well. I un-checked your answer even though it was really helpful. Jun5 comment Degrees of freedom in quadratic regression simultaneous confidence bands Right to the point. Thanks for the help! Updating my code now. May28 comment What is the difference between linear regression and logistic regression? Oops, you're right. Fixing it now. May24 comment How do I predict subject mean (w/ error) with repeated measurements in SPSS? Also correct. However, in order to have SPSS predict a value of concentration for my unknown, I need to input my read values as independent. I don't know how to have SPSS use a regression model to go from a measured dependent (read) to an estimated independent (concentration) with with error. May23 comment How do I predict subject mean (w/ error) with repeated measurements in SPSS? Correct. The only variation in them would come from random variation in the measurement itself. I should point out that these data are completely simulated. In reality, the "unknown" sample might have actually been measured from independent collections or something to that effect. Either way, I still want to know the same thing: How does one infer an unknown dependent when both the model-building data and the unknown inputs have uncertainty? Can this be done rigorously at all? in SPSS? May22 comment Interpreting a negative confidence limit for a proportion note: updated link