Linked Questions

0
votes
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. ...
0
votes
1answer
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 ...
5
votes
1answer
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?
6
votes
1answer
247 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 ...
0
votes
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 ...
1
vote
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 ...
0
votes
1answer
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 ...
6
votes
1answer
204 views

Is there a counterexample to the claim that throwing away “insignificant” predictors doesn't generally harm a model?

I have learned from this site (see question here), and from Frank Harrell's Regression Modeling Strategies that generally speaking one should not remove variables because they are insignificant. I was ...
1
vote
1answer
360 views

Is it ok to repeat MANCOVA until all interactions are significant

In order to enlighten the relation of three (related) scales describing psychopathology (1 anxiety score and 2 shame scores) with demographic data (gender, place of residence, age etc) collected by ...
0
votes
1answer
562 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 ...
3
votes
2answers
113 views

If a regression term doesn't do what it was intended to do, is it alright to remove it?

This is going to look like a duplicate of a common question--something along the lines of "Should/can I remove insignificant regression terms?" That type of question has been asked--and answered--here ...
0
votes
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 ...
3
votes
4answers
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 ...
0
votes
1answer
716 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 ...
0
votes
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 ...

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