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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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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+50

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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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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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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26 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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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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58 views

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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2answers
39 views

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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71 views

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 ...
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Mathematical Basis behind inflation of Standard errors of Regression estimates due to multicollinearity

We know that due to multi-collinearity, the standard errors of beta estimates get inflated. But what is the mathematical basis to it? I am looking for some mathematical relationship or something to ...
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VIF around 3.5 in two covariates, how shall I deal the problem?

in my multinomial logistic regression model (sample size n=290) I am adjusting the results for a group of covariates (n=8). I tested them for multicollinearity and if most of them have a VIF lower ...
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How to tell the difference between linear and non-linear regression models?

I was reading the following link on non linear regression SAS Non Linear. My understanding from reading the first section "Nonlinear Regression vs. Linear Regression" was that the equation below is ...
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High correlation between two independent variables, but no multicollinearity?

I have two independent variables which have a Pearson correlation coefficient of 0.98. The two independent variables measure the same underlying construct but only at two different points in time ...
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39 views

Interactions and Multicollinearity

I have a question concerning multicollinearity in OLS. I built a very simple model: y = a + x(x) + y(x) + e. In this case x is a significant predictor, y is not. Both of them are time related ...
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How can I compute multicollinearity (VIF) in R, and know if it's safe?

I am working on a group project for a university course on "big data research methods". My data is aggregated by neighborhoods in Chicago. My dependent variable $Y$ is the property crime rate (per ...
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111 views

Multicollinearity in polynomial regression

How to deal with multicollinearity in polynomial regression? Suppose I have $x$, $x^2$ and $x^3$ as independent variables in my regression equation. How can I calculate and remove multicollinearity ...
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Combining Collinear Variables

I have a set of 10 variables: 9 explanatory, 1 response. I wish to do a constrained regression on the variables and use the values of the coefficients as weights in a TOPSIS analysis. I am having ...
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113 views

What is the difference between VIF and stepwise regression?

What is the difference between the variance inflation factor (VIF) and stepwise regression as both help in detecting multicollinearity? What variables are different while running both techniques?
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Automatically fixing ill-conditioning or collinearity

I'm backtesting a regression model, which entails running it on a bunch of bootstrap samples of a "rewound" version of our data set. Unfortunately, in some of these resamplings, I end up getting some ...
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Linear regression multicollinearity

I run linear regression with Posttest scores as DV and Pretest scores and Group as IVs. Collinearity Statistics Tolerance shows .998 both for Pretest and Group (VIF 1.002). Is this one of the ...
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How to Deal with Multicollinearity?

I use cross-sectional macroeconomics variables with OLS. I found that my data suffers from multicolinearity and i am looking for solutions. I read about first differences of the variables and i tried ...
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Multicolinearity in logistic regression using R

Upon performing binary logistic regression, I have found VIF, using R programming, as follows: ...
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How do two variables that with a non-significant low correlation (<.10) become negatively associated in regression? [duplicate]

I have a correlation between variable A and B that is non-significant and small (r = .09). When examining a hierarchical regression with three control variables entered at step 1, and three variables ...