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### Appropriate Feature Selection methods

I'm running a multinomial logistic regression and I'm torn regarding which variable selection method to apply... The ones I know are backwards elimination or forward selection, chi square feature ...
18 views

### After the linear regression on the main predictors, how to include the interactions of them?

I'm currently using R to do a multiple linear regression with 7 main predictors. I've already completed the first step of regressing the dependent variable onto those main predictors. ...
38 views

### Variable selection for Cox regression repeated for multiple covariates of interest

I am doing a retrospective analysis of the effect of various measures of haemodynamics in sepsis on mortality. I will separately look at the effect of 5 independent variables: 1) shock index 2) blood ...
65k views

### What is the difference between “factors” and “covariate” in terms of ANCOVA? [duplicate]

I am a bit confused on the term "covariate". It seems like the term can mean two different things. In ANCOVA, the term is used for the third variable that is not directly related to the experiment. ...
2k views

### When is it better to use Multiple Linear Regression instead of Polynomial Regression?

In the course I've just learnt Multiple Linear Regression and Polynomial Regression. Why would you ever use Multiple Linear Regression when Polynomial Regression will always fit the data better?
2k views

### what to do if all control variables are insignificant and none affect the main effects?

All the control variables were there because they affected the dependent variables in previous studies. However in my dataset, all are insignificant and do not affect the main effects. Should I ...
248 views

### When the effect size of a covariate is high and yet not significant

I was reading this answer to the question on whether all covariates should be kept in the model or just those that are statistically significant, and I noticed the point number 2: The effect size ...
10k views

### How to identify which features are more likely to contribute to the desired outcome?

This question is in tandem with my earlier question here: Using ML approaches to build a recommender engine for sales team However, now I'd like to discover insights about x features as ...
718 views

### ROC analysis and death/recurrence as binary marker

I am doing ROC-curve analysis on my patient cohort, and I am wondering if it is statistically ok to use "death" and "recurrence" as the binary marker, even though these parameters will be influenced ...
564 views

### Linear Regression Feature selection: multiple-regression p-value filter versus Lasso / Recursive Feature Elimination

I have been thinking of this problem for days and I can't seem to arrive at a conclusion for feature selection in Linear Regression. Please tell me what is wrong with this simple approach versus ...
39 views

### Understanding regression modelling: 3 factors, 3 continuous predictors

I am a bit confused about how regression modelling works. I have a response $y$, 3 continuous predictors, and 3 factors. I don't have anything else available. I fit the model ...
55 views

### Is there anything wrong with this sort of model reduction (p value + AIC)?

I have fitted a model with 7-8 covariates. Here's what I do to reduce it: I first look at the p-values. I select all covariates with p-values > 0.05. Then I remove them one by one, get the AIC, and ...
106 views

### R - Analysis of a Qualitative Predictor with 30 levels [duplicate]

I'm running a multiple linear regression in R. In my linear regression I have 'country' as a qualitative predictor, which dramatically increases the adjusted R^2 value, and lowers my BIC. I want to ...
In my model I want to include two dummies ($d_1$,$d_2$) and also the interaction effects of these two dummies with another independent variable, $x_1$. The interaction terms are $x_1\cdot d_1$,\$x_1 \...