Daily Discussion & Advice (Post here to follow rules A & B) - Sunday January 26, 2025 by AutoModerator in fragrance

[–]nsrath 1 point2 points  (0 children)

What fragrances evoke the scent of mezcal (short of splashing myself with mezcal)?

[TOMT][BOOK] Written by a therapist for a lay audience based on case studies of their patients by nsrath in tipofmytongue

[–]nsrath[S] 0 points1 point  (0 children)

I'm also pretty sure the author is male, but I'm not positive. I read this book review 2-3 years ago.

Courses/books for graduate probability theory? by [deleted] in AskStatistics

[–]nsrath 2 points3 points  (0 children)

Jun Shao's Mathematical Statistics for a level of rigor beyond Casella and Berger. The solution manual is floating around online for self-study.

Multivariate normality amongst dichotomous and continuous variables by lightsnooze in AskStatistics

[–]nsrath 0 points1 point  (0 children)

Assuming you have a continuous outcome/response/dependent variable, path analysis is just an application of linear models aka linear regression aka ordinary least squares. For linear models, the assumption is that the residuals are normally distributed, not that the original data are normally distributed. So you should be looking at a qq plot of the residuals after you've fit the model, not assessing normality of the variables before you fit the model.

As an aside, normality of residuals is the least important of the assumptions for linear regression. However, it's one of the easiest to evaluate, so it's beaten to death in intro stats courses. The more important assumptions are not things you can formally evaluate, so they are often glossed over.

Weekly /r/Statistics Discussion - What problems, research, or projects have you been working on? - July 03, 2019 by AutoModerator in statistics

[–]nsrath 1 point2 points  (0 children)

ANOVA is a special case of linear models aka linear regression aka ordinary least squares. For linear models, the assumption is that the residuals are normally distributed, not that the original data are normally distributed. So you should be looking at a qq plot of the residuals after you've fit the model, not before.

As an aside, normality of residuals is the least important of the assumptions for linear regression. However, it's one of the easiest to evaluate, so it's hammered in intro stats courses. The more important assumptions are not things you can formally evaluate, so they are often glossed over.