# Linked Questions

1answer
55 views

### 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 ...
1answer
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 ...
1answer
4k views

### Are insignificant variables included in calculation of predicted probabilities?

When calculating the predicted probabilities in a logistic regression model, do we consider all the variables or just the significant ones? For eg: Let's say my model has: dependent variable Y and 3 ...
0answers
132 views

### Removing insignificant variables? [duplicate]

Suppose you fit a linear regression model on some data with 10 variables. The F-statistic shows that 3 of them are significant (p < 0.05) , 2 are within trend (0.05 < p < 0.10), and the other ...
1answer
429 views

### Should a non-significant adjustment variable be kept in a regression model? [duplicate]

I'm working with a structural equation model to study influenza infection risk. As age is a known risk factor to explain infection, I therefore adjusted my infection outcome on the subjects age class. ...
2answers
2k views

### How to remove one of the factor from the model?

We have a variable "pos" in the regression which has three values : guard, forward, and center. My regression looks like y~a+factor(pos) But one of the factor's value is insignificant (i.e. forward). ...
1answer
722 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 ...
1answer
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. ...
1answer
569 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 ...
2answers
224 views

### How to interpret logistic regression output? [duplicate]

I have the R output for the logistic regression model. It seems that only the intercept and psa are statistically significant. Does that mean I should remove sorbets_psa and cinko from my model and ...
1answer
40 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 ...
1answer
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 ...
1answer
108 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 ...
0answers
29 views

### Smaller p-values on a selective multiple regression analysis [duplicate]

When I run a multiple regression analysis in Excel on 20 independent variables and 1 dependent variable (in two goes), I obtain in the summary a set of p-values. When I select the (six) ones that are ...
0answers
191 views

### If removing another variable makes another variable insignificant should it be removed?

This is a logistic regression used for the goal of prediction. Originally a model had ten variables. Two variables were removed using a clustering procedure. Then one variable was removed due to ...

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