Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard errors are increased. Multicollinearity can have counter-intuitive effects.

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Multicollinearity in case of nonstationary variables

I am using dynamic OLS regression and fully modified OLS regression (in eViews) to estimate long run relationships' coefficients; my variables are all I(1). Do I need to check for multicollinearity ...
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Can I have an IV which does not have main effect on DV directly but might have an interaction effect with another IV on DV?

I am inducing envy (IV 1) to see the effect on focusing illusion/anchoring bias (DV). Since I am going to induce envy by showing attractive others' pictures,then gender will play a role because ...
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How to interpret a VIF of 4?

I am doing a multiple regression, trying to test the extent to which personal income changes and Presidential popularity can predict election results. I have a small sample size, unfortunately, as ...
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BIC difference for model selection when models have different (and correlated) predictors

I have a binomial dependent variable Y and two main IVs: A is categorical (5 non ordered levels) and B is continuous. A and B are collinear (I tested the effect of A on B with a linear model, and it ...
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Why is multicollinearity not checked in modern statistics/machine learning

In traditional statistics, while building a model, we check for multicollinearity using methods such as estimates of the variable inflation factor (VIF). But in machine learning, we instead use ...
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Correlation regression

I have a multiple regression model with 4 independent variables and 5 control variables. y=x1+X2+X3+X4+x5+X6+X7+X8+x9. and i have 4 null hypothesis to test the relationship between the independent ...
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Variable correlation and collinearity in logistic regression

Hoping to get some insight on the issue of correlation/collinearity in predictors for logistic regression. Let me preface this by saying I’m no statistician, but rather a GIS analyst with exposure to ...
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How can I check for collinearity and heteroskedasticity in mixed models with lme (package nlme)?

How can I check for collinearity and heteroscedasticity in lme (R package nlme)? I found a blogpost that provides functions to do so for package lme4, but not for nlme. Here's a minimal example ...
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P-Values decrease when additional significant variables added (multicollinearity?)

I am doing a study for my masters correlating two separate development indicators to election results for the incumbent government. Unfortunately, I was only only able to get 11 years worth of data ...
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44 views

Regression with two dummy variables: city and state in which one is implicitly included in the other: multicollinearity?

I am new to statistics and am really struggling with something that is probably easy for a statistics expert to answer. I am currently working on my dissertation in which I am trying to see whether ...
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4 continuous DVs, 12 continuous & discrete IVs: Issues of multicollineararity & multivariate question

I am conducting an analysis to see if various attributes about a set of stimuli affect the rates of different kinds of errors subjects make. I have 4 continuous DVs (error rates) I'd like to predict ...
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40 views

Multicollinearity problems with `polr` function in the MASS package for ordinal response [closed]

I've been trying to use the polr function for a couple days now. The dataset has lot of features (~70) and some of them are factor variables. When I run a simple ...
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Break an independent variable into two and collinearity? How to interpret the result

I am not sure if the title makes sense. Here is my situation. I am running a regression as below: $$ y = \alpha_0 + \alpha_1 T_1 + \alpha_2 Z + \epsilon $$ Where $T_1$ is my interested covariate: ...
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35 views

Model selection in mixed-effects model with collinearity trouble

In a model aimed to assess the influence of land use measures on ecosystem functioning, I have one log-transformed dependent variable (the ecosystem function), and 5 fixed-effects independent ...
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1answer
28 views

How to deal with different outcomes between pairwise correlations and multiple regression

I have different results from a correlation table and a multiple regression model. I know that it is an effect of multicollinearity because correlations up to $.474$ exist between predictors, but this ...
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Detect multicollinearity in maximum likelihood scenarios

I'm estimating a binary logit discrete choice model with BIOGEME and want to check for multicollinearity of my predictors. BIOGEME uses maximum likelihood estimation (MLE) and not ordinary least ...
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Variable selection with longitudinal and correlated data

I'm working with a high-dimensional medical database, with detailed monthly medication reimbursement data as well as occurence of diverse medical outcomes, over several years (2010 to 2013). My ...
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182 views

Why is post treatment bias a bias and not just multicollinearity?

In this presentation by Gary King, he discusses post treatment bias as follows: Post treatment bias occurs: when controlling away for the consequences of treatment when causal ordering ...
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45 views

How can I check whether multicolinearity exist between categorical variables or numerical and categorical variables?

I did a linear regression with 10 variables, including categorical and numeric variables. But although my $R^2$ was 0.8 there were only 2 variables that were statistically significant. Am I correct ...
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If x2 & x3 affect x1, & x1 affects y, should x2 & x3 be included in a regression model?

Let's consider the regression $y=x_1+x_2+x_3+\varepsilon$ It is known that $x_2$ and $x_3$ affect $x_1$, but $x_2$ and $x_3$ do not affect $y$. $x_1$ can affect $y$, but only to a small extent. The ...
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2answers
65 views

Adding up regression coefficients in the presence of multicollinearity and reversal of signs?

My time series model suffers from multicollinearity between two independent variables. When taking one of the variables out of the model I obtain a coefficient of 0.08 for variable 1 but when ...
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29 views

Conditional Independence vs. Collinearity

I know that when two predictor variables are correlated then that increases error and distorts the results, which is called multicollinearity. But what is conditional independence, is it the same as ...
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Plm is bad at auto-dropping collinear variables

I am having the same problem as this question. Someone answered with: "I've had similar problems. Plm is bad at auto-dropping colinear variables. Maybe check to ...
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Finding multicollinearity from data with missing values

I have a data frame with around ~30 columns and at least 20 features have missing data in between 60-70%. I am wondering if it is possible to calculate multicollinearity in this case. If yes, how? My ...
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Multicollinearity for interaction

My model has some multicollinearity issues: x1 (continuous independent variable), x2 (categorical independent variable), x1*x2 (interaction term), and y (dependent variable) SPSS shows me that the ...
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Logistic regression, separating variable (moderator) true in population! [duplicate]

I already checked other posts in this area, but still couldn't get a fit to my issue: I have the following preconditions: Software: preferred SPSS v21, possibly R Sample size: 5655 (will get around ...
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How we can select suitable Variance Influence Factor (VIF) critical value to detect collinearity?

In Variance Influence Factor(VIF) we should use a critical value. A rule of for this value is 10. Is this a good value for detecting collinear based one ...
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141 views

How should strongly correlated covariates for logistic regression be treated?

I have to build a multiple logistic regression model with two strongly correlated covariates (predictor variables). How should they be treated? Am I to exclude one of them from the regression? There ...
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two stage? step? regression - passing covariance matrix

I received the following email from one of my colleagues/superiors this morning. I was hoping someone might be able to help me interpret the question and provide a response. I just started this job ...
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Stata automatically tests collinearity for logistic regression?

I'm using Stata for logistic regression. This software automatically checks for collinearity and remove (drop) some variables as we can see below: ...
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Dummy Data & Regression Analysis

I've been doing some analysis projects at work and I've been supplied with some dummy data regarding whether an applicant has applied on a weekday or weekend (I have set this as 0 for weekend and 1 ...
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How to Test Collinearity Between GROUPS of Predictors?

I had a model (made with VW, log loss) based on a set of base (p=1000's) predictors. It did not predict well. I added set A of predictors (p=~5 predictors), and it improved immensely. I added set B ...
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Sign change of a coefficient in logistic regression? [duplicate]

I am running a logistic regression with 5 continuous independent variables (IV). The problem is that IV4 when taken alone has a positive correlation with outcome (coeff > 0), and when taken with the ...
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Multicollinearity and the intercept term with categorial variables

We're given a regression equation with two dummy variables which are perfectly collinear. $$ y_i = \beta_1 D1_i + \beta_2 D2_i + e_i$$ where $ D2_i = 1-D1_i$. Can we estimate this model using least ...
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How does the GLM handle collinear predictors?

In the case of an ordinary least squares GLM with two nearly collinear predictors, how does this shared variance get reflected in the parameter estimates? My understanding is that the parameter ...
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percentages as independent variables and risks of multicollinearity

I am working on a model in which I would have percentages among my independent variables. To be more specific, I would include in the model the variables that, together, would attain a 100 (say I want ...
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Multicollinearity to be assessed item wise or construct wise

In order to check multicollinearity, do I have to combine items for the same construct and assess the correlation table for the constructs, OR do I have to check the item wise correlation table?
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Whether to use height or its z-score or percentile?

I am trying to do regression analysis with level of a chemical in blood as dependent variable and age, gender and weight of children as predicting variables. The sample size is about 5000. Age and ...
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How to systematically remove collinear variables in Python?

Thus far, I have removed collinear variables as part of the data preparation process by looking at correlation tables and eliminating variables that are above a certain threshold. Is there a more ...
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When and when not to use ridge regression

What are the 'indications' (i.e. when to use) and 'contra-indications' (i.e. when not to use) of ridge-regression. I tried to read up on the net and it seems to useful when multi-collinearity is there ...
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How to interpret ridge regression plot

Following is the ridge regression example in MASS package: ...
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Variable selection with hand on example in R

I am looking for most suitable way to perform analysis (with statistical evaluation) where the aim is to find (select) a suit of continuous (collinear) variables that best describe other continuous ...
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Including interactions into the principal component regression

I have a linear model that requires a large number of interactions (there are as many interactions as there are IVs) and I want to reduce collinearity using PCA, then regressing the DV on the ...
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linear regression - modelling explanatory variables which depend on each other

I'm trying to estimate the value of an apartment, by doing a regression through similar apartments. The regression model looks now like this ...
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Multicollinearity Diagnostics of Mean-Centered Interaction Terms

Interaction terms in moderated regressions exhibit high multicollinearity due to the high correlations with their main effects in case of uncentered data; for Normal distributed variables this is not ...
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Multicollinearity/Redundancy

In a regression problem would it be a problem if I included redundant variables? Ie. Total number of red birds, total number of blue birds, total number of red and blue birds. That would blow up my ...
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Example of interpretation of logistic regression

I was looking at a paper by Pell JS 2009, regarding smoking and survival following acute coronary syndrome. Part of the analysis carried out in the paper was a logistic regression with results as ...
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B is significant correlated with Y, but not a significant predictor of Y in a multiple regression. What can it mean?

I'm working on a paper and I have some problems explaining some of my findings. I have four independent variables, let's call them A,B,C,D. And i have one dependent variable, let's call it Y. In my ...
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Determining Multicollinearity for the given ScatterPlot Matrix

Given this data: should I be concerned with multicollinearity here?
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Is Support Vector Machine sensitive to the correlation between the attributes?

I would like to train an SVM to classify cases (TRUE/FALSE) based on 20 attributes. I know that some of those attributes are highly correlated. Therefore my question is: is SVM sensitive to the ...