Linked Questions

231
votes
46answers
23k views

What are common statistical sins?

I'm a grad student in psychology, and as I pursue more and more independent studies in statistics, I am increasingly amazed by the inadequacy of my formal training. Both personal and second hand ...
65
votes
18answers
88k views

Statistics interview questions

I am looking for some statistics (and probability, I guess) interview questions, from the most basic through the more advanced. Answers are not necessary (although links to specific questions on this ...
6
votes
3answers
3k views

Is this really perfect separation in logistic regression, or is something else going on?

I have some data on patients presenting to emergency departments after sustaining self-inflicted gunshot injuries, stored in a data frame ("SIGSW," which is ~16,000 observations of 47 variables) in R. ...
4
votes
3answers
8k views

Variable is significant through stepwise regression but not in final model's summary; which should I report?

I used generalized linear mixed models (with the glmmADMB package) to identify environmental factors related to parasite abundance in rodents. I used stepwise ...
4
votes
1answer
6k views

Forward or backward sequential feature selection?

I was trying to carry out feature selection on a dataset using sequential feature selection. The dataset contains more than 5000 observations (rows) and 22 features (columns). Now I see that there are ...
5
votes
3answers
26k views

How to use R anova() results to select best model?

Newbie question using R's mtcars dataset with anova() function. My question is how to use anova() to select the best (nested) model. Here's some example data: ...
4
votes
1answer
4k views

Should I use ANCOVA or multiple regression with dummy variables?

I am writing a manuscript using an experimental design which predicts interactions between 1 continuous variable and multiple dichotomous variables, all predicting a continuous variable. As is ...
4
votes
3answers
373 views

What variables need to be controlled for in regression?

There are numerous discussions on this site concerning how to control for certain variables in regression analysis. How exactly does one “control for other variables”? How do you "control" ...
3
votes
2answers
702 views

Significant differences among fit lines - ANCOVA not enough?

I often am in the situation of having data sets consisting of an independent variable, a dependent variable, and a factor with multiple levels - for instance, calibration curves for an instrument ...
1
vote
2answers
1k views

Stepwise model selection using Generalized Akaike Information Criterion

I run a series of models using gamlss stepGAIC() model selection. The problem that I have is that in gamlss, ...
5
votes
1answer
1k views

Alternatives to stepwise discriminant analysis for feature selection on hyperspectral data

I am new to R and to hyperspectral data analysis. However, in my research, I have found that many warn against using Stepwise discriminant analysis (using Wilk's Lambda or Mahalanobis distance) for ...
3
votes
1answer
2k views

Repeated measures - random effects for logistic regression in R?

Study design 504 individuals were all sampled 2 times. Once before and once after a celebration. The goal is to investigate if this event (Celebration) as well as working with animals (sheepdog) ...
1
vote
0answers
2k views

R Linear model step NA values

My aim is to carry out a generalized linear model (glm) with 1 response variable and 13 explanatory variables.Unfortunately 3 out of the 10 explanatory variables contain NA values (2/3 of data set of ...
4
votes
1answer
708 views

GAMLSS: model with interaction terms failed

I use gamlss method from library(gamlss) on my full models with interaction terms and try to reduce them with stepGAIC. There are 3 things I want to ask. Do I have to specify a link for the model? ...
2
votes
1answer
615 views

How does step function selects best linear Models which includes polynomial effects and interaction effects in R?

I try to find "best" linear models with continuous and categorical covariables with Interaction Effect by BIC. The continuous covariables should have a quadratic effect on the response variable. ...

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