Why are my posterior expectations not lining up with my sample averages? It still forms a linear relationship, but my hierarchical normal model doesn't seem to be predicting well. Is it because of the prior parameters? Graph submitted by /u/life453 [visit reddit] [comments ..read more

Indirect effect is significant but direct effect is not I am running a parallel mediation Hayes model where the total effect is significant, the indirect effect of one of the mediators is significant/the other is not, and the direct effect is no longer significant after accounting for covariates and the mediators. How can I explain this in writing? submitted by /u/hellospacecommand [visit reddit] [comments ..read more

If my goal is industry, I had considered the path of industry after my degree rather than a PhD. However, I wonder what the career trajectory is for MS statisticians who go into industry. How technical can your job remain before you must consider management roles? Can you stay in a technical role for majority of your career? Was not doing a PhD in stats worth it for your career? Did your pay stagnate without a PhD? submitted by /u/AdFew4357 [visit reddit] [comments ..read more

Interested in grad school for Statistics or Data Science. I'm a first-year undergrad pursuing B.S. double major in Statistics and Business Analytics with a minor in Data Science (no Data Science major here, just a minor ?). My school isn't widely recognized but is academically rigorous and ranks decently (T50 on U.S. News, bottom half). As I near the end of my first year, I'll have a GPA of 3.79. While it isn't bad I'm very unhappy with it. 3.79 is nowhere near a GPA I need for the competitive programs I'm interested in, but I have time to improve it. I'm aware of the general advice like main ..read more

Hello everyone, I'm currently conducting a Generalized Linear Mixed Model (GLMM) analysis in SPSS. I'm interested in applying post-hoc tests, specifically Tukey or Bonferroni, to further analyze my results. However, I've encountered some difficulty in finding the appropriate procedure within SPSS. Could someone please guide me on how to apply Tukey or Bonferroni post-hoc tests in SPSS? submitted by /u/yagizdemir [visit reddit] [comments ..read more

So I applied logistic regression. DV is 10year risk which itself is derived from a certain scale. Ok so age is one of the few category in that scale to assess 10yrs risk. So in the logistic regression (where DV is 10yr risk) for covariates like age (which have been used to assess the 10yr risk) have huge odds ratio while the other covariates that did not belong to the scale have normal odds ratio. What is the likely explanation and how should i proceed futher? submitted by /u/croissantlover92 [visit reddit] [comments ..read more

Might be a stupid question but what is the underlying probability distribution we use in the ordinal logit/probit models? Obviously the logit/probit parts specify the link function but for binary data we typically use the Bernoulli distribution and for nominal outcomes we often use a categorical distribution (maybe that changes with conditional logit/multivariate probit models), I was wondering what distribution we use for the ordinal model? submitted by /u/karateteacher01 [visit reddit] [comments ..read more

I’m quite new to JMP, and was wondering if I could input the formula for my own regression model I made, in the fit model section? I’ve looked up a few solutions but none work. On JMP Pro 17. Thanks! submitted by /u/camboats [visit reddit] [comments ..read more

Something I've noticed is that when using collapsed gibbs sampling to fit a Bayesian models (like Latent Dirichlet Allocation, Dirichlet Multinomial Mixture models, or this Citation Influence model), it seems like we only compute MAP estimates for the parameters that are "collapsed out." I'm working on a project right now where it would be really useful to be able to compute the full posterior for these parameters, mainly to get a good sense of the uncertainty in these terms. Intuitively it feels like this should be possible, since this paper (equation 8) seems to suggest that the posterior f ..read more

Hello, 9 doctors will rate 200 patients. Each patient will receive 9 scores for a numerical (integer) variable (urgency, 1 to 10) and 9 scores for a categorical variable (improvement, low/mid/high). How can I normalize these scores into two single numbers (0-1)? My plan is to turn them into weights for creating a prioritizing list I would need something like: Patient #1, urgency 0.22, improvement 0.37. Patient #2, urgency 0.44, improvement 0.70. For the numerical variable: Do I average the doctors' scores and then min-max normalize it? Can I normalize it by a Z score? What if it's not normall ..read more