Questions tagged [collinearity]
The collinearity tag has no summary.
30 questions
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Media Value Simulation ( Regression)
I am estimating a regression model to predict media value and later use residuals for Monte Carlo simulation.
The model includes:
• Market fixed effects (grouped)
• Asset categories
• A hierarchical ...
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2
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1
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please make a distinction between sample correlation-coefficient and a linear correlation coefficient?
Sometimes,it comes to my mind that the two concepts are distinct concepts. The sample correlation coefficient differs from a linear correlation coefficient. Please be sure that I do not have have any ...
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7
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1
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differentiate between collinearity and correlation coefficient?
The two terms:collinearity and correlation coefficient are freqently used in statistics. Could you please help me understand in ordinary language the difference in two concepts.
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4
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Is it statistically wrong to adjust for sex and race then do subgroup based on them?
I am doing for subgroup analysis of early mortality (Outcome) based on Transfusion(WITH ADJUSTMENT for both ...
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Why Multicollinearity is a problem in machine learning algorithms
Is only a subset of algorithms are affected by the multicollinearity problem or all the machine learning algorithms?
What is the solution for this?
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Does Multicollinearity affect Neural Networks?
Can someone explain to me like I'm five on why multicollinearity does not affect neural networks?
I've done some research and neural networks are basically linear functions being stacked with ...
2
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0
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Deriving VIF equation from the matrix form of Least Squares equation
I have been working through the derivation of the formula used to calculate the Variance Inflation Factor associated with a model. I am hoping to start with the Least Squares equation as defined in ...
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Multicollinearity vs Perfect multicollinearity for Linear regression
I have been trying to understand how multicollinearity within the independent variables would affect the Linear regression model. Wikipedia page suggests that only when there is a "perfect" ...
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2
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Does PCA helps to include all the variables even if there is high collinearity among variables?
I have a dataset that has high collinearity among variables. When I created the linear regression model, I could not include more than five variables ( I eliminated the feature whenever VIF>5). But ...
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How does tree-based algorithms handle linearly combined features?
While I am aware that tree-based algorithms (e.g., DT, RF, XGBoost) are 'immune' to multi-collinearity, how do they handle linearly combined features?
For example, is there is any additional value or ...
- 755
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Whether Interaction terms should be included in Linear Regression analysis?
I am working on a linear model with 6 independent variables and when thinking about including an interaction I got lost.
An interaction exists if the level of one independent variable is affected by ...
- 33
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Understanding one of the assumptions of linear regression: Multicollinearity
I've read that multicollinearity is one of the main assumptions of multivariate linear regression - Multicollinearity occurs when the independent variables are too highly correlated with each other.
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Possible harm in standardizing one-hot encoded features
While there may not be any added value in standardizing one-hot encoded features prior to applying linear models, is there is any harm in doing so (i.e., affecting model performance)?
Standardizing ...
- 755
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Collinearity between continuous and categorical variable
I have a medical dataset with features age, bmi, sex, gender, # of children, region, charges, smoker. Here smoker, gender, sex and region are categorical variables and others are numerical features. ...
4
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what is the difference in terms namely Correlation, correlated and collinearity?
A website says Correlation refers to an increase/decrease in a dependent variable with an increase/decrease in an independent variable. Collinearity refers to two or more independent variables acting ...
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