Linked Questions
31 questions linked to/from Can I ignore coefficients for non-significant levels of factors in a linear model?
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Is it advisable to drop certain levels of a categorical variable? [duplicate]
Let's say that I have one categorical variable with six levels, and I then create five indicator variables in order to represent the six levels. If two of the five variables are insignificant, then do ...
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2
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What to do with dummy variable that is not significant? [duplicate]
I´m running a binary logistic regression to predict the purchase probability for a product. My model contains mostly dichotomous and categorical variables.
One variable, let´s say "decision maker", ...
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1
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If a factor variable is to be dropped in model selection, should all levels be dropped simultaneously? If so, why? [duplicate]
In answer to a previous question factor pooling in model selection was discussed.
If a factor or categorical variable is to be dropped in model selection, should all levels be dropped simultaneously? ...
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1
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How to Interpret p-value for categorical variable in multiple linear regresion? [duplicate]
I have a query on how to interpret the result for multiple regression with categorical variables. I have categorical variable called Stay_In_current_city_years which has 5 levels. After running the ...
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0
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GLM and categorical variable R - remove one category [duplicate]
I am currently running quasipoisson models with a continuous response variable and 13 covariates. I am using glm() and summary()....
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0
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How to drop certain values for a factor variable while fitting a GLM? [duplicate]
My response var is a binary variable. In the predictor variable i have a type variable with levels as l1,l2,l3,l4. And when i run a logit (glm(redonse ~ type, family = "binomial"), Some levels of type ...
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1
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What to do with statistically insignificant dummy/categorical variables? [duplicate]
From the research I've done the common answer is that you can not remove insignificant dummy variables from a regression. I'm having a hard time finding academic papers or books that back up this ...
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2
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R linear regression - how to handle when some factors significant while others aren't [duplicate]
I'm playing with the Titanic data set, and trying to figure out what to do about the results I got from a lm that predict the age of the passenger.
How should I handle the Cabin values? Some Cabin ...
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1
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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 ...
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0
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59
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Dropping dummies from regression by putting them into the reference group [duplicate]
I have the following result of a logistic regression:
...
0
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0
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Does it make sense to only drop a specific level of a categorical variable? [duplicate]
I don't have SAS and the dataset with me, so I made up this table (from my memory). Basically this is what I got:
After deciding to leave the variable $age$ and $risk$ in my model, I created this ...
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GLM Logistic regression in R: one category is significant, but others are not. Should I drop the variable? [duplicate]
so I am using GLM for logistic regression in R and I have some variables with many factors.
I ran the model and has the result like this:
My question is:
1. Is this variable significant? ...
253
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8
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126k
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Algorithms for automatic model selection
I would like to implement an algorithm for automatic model selection.
I am thinking of doing stepwise regression but anything will do (it has to be based on linear regressions though).
My problem ...
107
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6
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41k
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Principled way of collapsing categorical variables with many levels?
What techniques are available for collapsing (or pooling) many categories to a few, for the purpose of using them as an input (predictor) in a statistical model?
Consider a variable like college ...
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4
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How to apply coefficient term for factors and interactive terms in a linear equation?
Using R, I have fitted a linear model for a single response variable from a mix of continuous and discrete predictors. This is uber-basic, but I'm having trouble grasping how a coefficient for a ...
4
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2
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Should the final R glm include only significant levels of factors
I am running a glm in R on data with quite many predictors (~50), both initially continuous and factors. The response is binary and the volume of the data is OK (~100K rows), in order to model non-...
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2
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If I have one non-significant factor level in a glm, is that entire variable now considered non significant?
I have a question similar to this one, but I just wanted to follow on and ask if the entire variable is now insignificant? I have a factor with 3 levels. When doing the model simplification, it showed ...
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2
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Regression with categorical predictors - use only some dummy variables [duplicate]
I am working on a regression and I have a factor variable "Marital Status"
Marital status has 5 levels: Single, Married, Divored, Widowed, Other (don't ask me what constitutes someone being an 'other'...
3
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1
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4k
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Categorical independent variable with three levels and binary logistic regression
I want to learn which level of a categorical independent variable should I look to interpret the odd ratios in binary logistic regression. For example, I have one independent categorical variable (...
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1
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2k
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Does it make sense to apply recursive feature elimination on one-hot encoded features?
Does it make sense to apply recursive feature elimination on a feature set pre-processed with One-Hot Encoding?
This is my code for feature selection:
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4
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1
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700
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When LASSO selects only parts of a categorical variable?
I want to use LASSO to construct a model and then run a logistic regression on the variables LASSO selects. However, LASSO selects only parts of some categorical variables that I put into it.
Does ...
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2
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When fitting a multiple linear regression model, if one factor is insignificant, should I refit another model?
Let's say, I have:
$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3$
I fit a multiple linear regression (MLR) model (lm() command) in R, and see a very large $...
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1
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732
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Can I perform RFE on a Categorical variable with multiple values?
I have a dataset with a variable called attributes and it is an "array" of strings.
E.g.
...
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1
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876
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feature selection of sub-categorical data on a linear regression model
I have a data-set with 7 features, 6 numeric and 1 categorical.
the categorical data is "Species", which has the ability to be sub-categorized (species, genus, family, order, etc...).
I want to build ...
0
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0
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914
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Interpreting regression coefficients with a multi-level categorical variable [duplicate]
How do I interpret the coefficients of a Regression with 1 continuous + 1 categorical predictor (with 4 levels - e.g., months)
Specifically, is the 1st coefficient equal to that of the 1st month or ...
2
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0
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818
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Feature Selection with Categorical Variables: Multicollinearity and Statistical Significance
Building a logistic regression model with three categorical features and one continuous. For simplicity, let's say I have the following features and variables:
...
2
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0
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721
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Recursive feature elimination on just the train data or complete dataset
I am using RFE with logistic regression. I will also be doing cross validation with RFE (RFECV in sklearn) to get the optimum number of features.
I am not sure whether to use RFECV on just train data ...
2
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0
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240
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Split Factor Levels Or Not In Variable Selection
This question is related to previous ones but I believe distinct. I am primarily interested in prediction and I have access to LASSO variable selection (but without factor level grouping) using the ...
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0
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230
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Significant variables with insignificant levels and vice versa in logistic regression
I hope all of us get well during the pandemic.
I have conducted an analysis using binary logistic regression to investigate the interaction between gender (male, female) and official language ...
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1
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Is choosing only significant coefficients (based on t stats) from a multiple linear regression model a good idea, provided the F stats is significant?
I doubt if this topic has already been discussed here. I did search the forum before posting this question and read similar posts, however unable to find my answer, perhaps due to my limited ...
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Logistic regression with only 1 dummy variable
I have the following dataframe in Python:
...