Linked Questions

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1answer
2k views

Main Drawbacks of stepwise regression [duplicate]

People typically prefer the Lasso or other methods to stepwise regression. What are the main problems in stepwise regression which makes it unreliable specifically the problems with forward selection ...
0
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0answers
616 views

p-values for feature selection [duplicate]

I am doing multiple regression analysis, in which i want to eliminate some of the insignificant features. In most of the machine learning books subset selection, shrinkage methods or PCA is used for ...
0
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0answers
473 views

Chi Square and t test to select variables for logistic regression [duplicate]

I need to build a logistic regression model. there are around 50 categorical variables. So, is this approach to select variables wrong?: do a chi square test of dependent variable vs independent ...
1
vote
1answer
459 views

How to choose predictor variables for GLM / GLMM from rather large data set? [duplicate]

I have about 80 predictor variables (with some multicollinearity, I assume) and a non-normal count data response variable (n=570) which is arranged into groups (n=34). I need to reduce the number of ...
1
vote
0answers
207 views

Why avoid stepwise regression? [duplicate]

I have been using model averaging and model selection bases on AIC and BIC for a while. I have recently discover the stepwise regression technique and I found a lots of people critize this methods. ...
0
votes
1answer
125 views

Chi square and logistic regression [duplicate]

Before running a binary logistic regression model i was interested to know the strength of association between IV and DV but for some independent variables the results came out to be insignificant.. ...
0
votes
1answer
35 views

In stepwise regression, how to interpret non-significant variables? [duplicate]

I have more than 15 IVs such as age, gender, education, first language, technology proficiency, health condition, etc, and one of my DVs is health literacy level, which is measured through a standard ...
1
vote
1answer
61 views

Beginner - Iteratively adding terms to regression model? [duplicate]

I'm learning about regression models via Andrew Ng's Coursera course. I have a question regarding automatically finding a good model. Does it make sense (my guess is no) to iteratively add terms, or ...
3
votes
0answers
61 views

Logistic Regression Model Selection Criteria [duplicate]

I'm having a go at coding a logistic regression model building algorithm and I'd appreciate some advice. I've read in several places (including here) that minimizing both AIC and BIC could be an ...
32
votes
3answers
9k views

Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors?

It is well known that researchers should spend time observing and exploring existing data and research before forming a hypothesis and then collecting data to test that hypothesis (referring to null-...
34
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7answers
105k views

Choosing variables to include in a multiple linear regression model

I am currently working to build a model using a multiple linear regression. After fiddling around with my model, I am unsure how to best determine which variables to keep and which to remove. My ...
55
votes
2answers
2k views

A more definitive discussion of variable selection

Background I'm doing clinical research in medicine and have taken several statistics courses. I've never published a paper using linear/logistic regression and would like to do variable selection ...
15
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5answers
9k views

Can I ignore coefficients for non-significant levels of factors in a linear model?

After seeking clarification about linear model coefficients over here I have a follow up question concerning non-signficant (high p value) for coefficients of factor levels. Example: If my linear ...
13
votes
6answers
8k views

Multicollinearity when individual regressions are significant, but VIFs are low

I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. ...
12
votes
5answers
41k views

Feature Selection Packages in R, which do both regression and classification

I am very new to R. I am learning machine learning right now. Very sorry, if this question appears to be very basic. I am trying to find a good feature selection package in R. I went through Boruta ...

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