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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check multicollinearity before regression in R

I want to check multicollinearity to avoid any redundancy in my database before doing the multinomial logistic regression with categorical dependent variable using R, knowing that the majority of my ...
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lateral collinearity

Is there somebody who could, in a reasonably general way, explain "lateral collinearity" and its practical implication in logistic regression? Does lateral collinearity necessarily imply strong ...
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64 views

Multicollinearity with highly safe t-statistics but VIF of 13

If all of my coefficients in my logsitic model have really perfect t-statistics that all show sufficiently high significance but have two coefficients that have high VIF like 13-14 with sample size of ...
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Can you create a mixed effects General Additive Model?

I'm facing a challenge with how to apply a GAM to a dataset. I'm using network data, pretend for the sake of specificity, between individuals. I'm interesting in the relationship between team size and ...
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Is multicolinearity problem ignorable under this situation?

When I run the logistic regression, two independent variables have VIF values greater than 10 like 13 or so. Logistic regression is the one I will use to measure the overall change in the dependent ...
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526 views

Can I ignore multicolinearity problem if all the regression coefficients are highly significant? [duplicate]

Can I ignore multicolinearity problem if all the regression coefficients are highly significant? My data is large enough (i.e. I have several regression models where each of the data points for them ...
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24 views

multicollinearity in logistic regression

Which is the best way to check for multicollinearity between two binary explanatory variables in logistic regression..? I use SPSS, if anyone could answer with special regards to that programme it ...
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45 views

Does the order of entering predictors in multiple regression model change the standardised Beta coefficients?

I am reviewing a number of research papers regarding domestic space heating energy consumption which used multiple regression techniques to identify the main determinants of space heating in the ...
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Centering variables for interaction, continuous x catagorical (2 levels)

For a multiple regression analyses I'm looking to assess whether an effect of a contiuous independent variable on a continuous dependent variable is different between two levels of a catagorical ...
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Multivariable Regression on correlated / collinear variables

Let us consider a variable $Y$ on which we want to apply a multivariable linear regression on the variables $X_1$ and $X_2$. $X_1$ and $X_2$ are collinear by construction, with $X_1=ab$ and $X_2=bc$, ...
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Detecting collinearity in Logistic Regression model

I'm running a predictive model using the logistic model in SAS and, currently, I'm trying to perform some diagnostics about the collinearity issue in the estimated model. To do that, I followed step-...
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10 views

Principal Component Analysis handled multicolinearity in data [duplicate]

How principal component analysis handled high multicolinearity in data set?
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28 views

Multicolinearity Test for Multiple Multivariate Regression

I have multiple independent variables and multiple dependent variables, some categorical and some quantitative. I have created a data sheet with dummy columns appropriate to each categorical variable. ...
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Explaining suppression effects in mediation analysis

The study focus is mediational effects of spirituality on the relations between engagement and wellbeing. Rather than the mediator decreasing the relations between engagement and wellbeing when it ...
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Conflicting intuitions re linear systems in levels vs shares

Suppose I have a set of components that sum to some total value, different in each period. Call the total Y. I have a set of equations, linear in the (sometimes transformed) variables, each ...
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23 views

How to calculate variance inflation factor (VIF) for a GLMER?

I've never worked with GLMER models before and I was wondering if calculating VIF is in any way different than in simpler regressions. In R, for instance, I usually use the ...
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22 views

VIF interactions

I would like to check for multicollinearity in logistic regression analysis. Independent variables are categorical (always binary) and continuous. Sample has limitted size (N=176, 36 events), so I can ...
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10 views

Univariate analysis, Multicollinearity

I am developing multivariate binary logistic regression model in order to find out risk factors for death (outcome). Should I use univariate logistic regression in order to "reduce" predictor´s list ...
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33 views

log log model: multicollinearity and interpretation

I would like some advice on a small multiple regression model. The model is a log - log one, with log(investments) as the dependent variable. My issue is that I would like to introduce log(GDP per ...
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31 views

Collinearity testing between predictors

I would like to test a collinearity between possible "predictors (risk factors)" for binary outcome (death). Possible "predictors" are categorical (always binary) and continuous... For two ...
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77 views

Lasso will not remove correlated variables

The very essence of lasso is that it is supposed to select only one of two correlated variables. However, when I include two highly correlated predictors (they are correlated with each other at level ...
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What is the difference/relation between variables that are multicollinear, confounding, interacting

What is the exact difference between two variables that are multicollinear and two variables that are interacting and two variables that are confounding? Are they multiple meanings for the same thing?...
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26 views

Colinearity and eigenvalue

I'm doing a binary regression with two predictors, one continuous and one binary. To check for collinearity I looked at the correlation .444, at the VIF 1.13 and 1.18 and the % of eigenvalue share. ...
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23 views

Shapley Value Regression for prediction

I've been successful in using the relaimpo-Package for R in SPSS through STATS_RELIMP to calculate the Importances of different predictors (in cases of multicollinearity). What im wondering now is how ...
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Does the variance inflation factor make sense for regularized regression?

I have a logistic regression model fit using L1 regularization. There are two variables that entered the model that have a correlation of over 0.90. The VIF for these variables are each about 60 which ...
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Strange outcomes in binary logistic regression in SPSS

I did a binary logistic regression with SPSS 23 and I found some strange outcomes. This is for NOACprev until No_Prev_treatment, the last 6 variables. First of all they have very high outcomes for B, ...
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Statistic test for correlation among multiple variables || statistical test for confirming sales pattern among multiple items

While thinking about sales of a company, I came across a question. I have data like below: Day1 day2 ......day30. item1 10. 20. ....... 30. ...
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Multicollinearity - continuous and dummy variables

I know that one of the assumptions of Gauss-Markov is no perfect multicollinearity. If I want to run a model that estimates the effect of gambling on wages, would this model be appropriate: Wage = ...
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Does practical insignificance mean no relationship?

I have two problems: 1) I have a regression coefficient that is very significant (large dataset), but has low practical significance. Can I say there is no relationship? And I mean really tiny, like ....
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How to control serially correlated independent variables?

I'm interested in studying the impact of one variable (e.g., R&D expense at year T) on future firm performance (e.g., Sales in year T+5), I know it's incorrect to specify the following model: ...
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Gross capital and consumption expenditure

I am doing a multivariate regression with gross capital formation and final consumption expenditure as the dependent variables. This is more in the realm of economics, but will the nature of the two ...
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21 views

Can feature with high positive correlation have opposite weights sign? [duplicate]

I have two features for a binary classification problem which are highly positively correlated. (0.79) But when I build a logistic regression classification then I see their weights are opposite in ...
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Should the intercept be included when you check the condition index?

Many sources state that a condition index >30 constitutes a multicollinearity problem. When I've tried to implement this check in practice, I've realized that the condition index (and VIFs) change ...
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Perfect collinearity between one level of two categorical variables

I am setting up a logistic regression model with mostly categorical independent variables (answers to survey questions). Some of the variables have levels like "High-Medium-Low-NotApplicable". The ...
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Why aren't we simply using $R_j^2$ instead the VIF?

After all we calculate the VIF by $1/(1-R_j^2)$. A VIF of $5$ corresponds to an $R_J^2$ of $0.8$. To me, the information given by $R_j^2$ just becomes more obscure when I apply the VIF formula. Why ...
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Why are VIFs below ten not still considered very worrying?

I've been trying to read up on multicollinearity, and I think I have a decent grasp of it, and of what VIF tells me. But there is one aspect of the advice that seems quite universal, but makes me ...
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Understanding multicollinearity and bias in coefficient estimates

In trying to better understand the effects of multicollinearity within the context of logistic regression, I have come across the following quote from Paul Allison's textbook: Although ...
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Is it OK to use an original variable & another variable constructed from it in a regression model if there is no multicollinearity?

I'm doing binary logistic regression. I want to predict the chance of being in an advanced class. There no multicollinearity among my variables. I have three predictors: If you passed the test or ...
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Potential multicollinearity problem between a dummy and the constant term

In an OLS regression, I need to use a variable that is equal to 0 for all observations (1636) except 3 of them. I am afraid that this may generate a multicollinearity problem with the constant of the ...
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How does a non-random sample limit Logistic Regression

I have been attempting to run a binary logistic regression and have come into some difficulties with it. The outcomes I have got are OK for odds ratio, but the space between the lower and upper ...
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Collinearity in Classification Model for Churn Prediction

I'm working on evaluating various classification algorithms to help predict customer churn (or at least ID interesting features to use in later strategy). The goal is to identify accounts who are at ...
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Should multicollinearity problem be looked into while doing cointegration?

Multicollinearity and Cointegration is not the same thing however, if the series actually move together in the long-run i.e. are cointegrated wont they also be collinear making e.g. Autoregressive ...
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Issues of collinearity in Granger Causality testing

If I have a regression where a lagged explanatory variable is regressed on a dependent variable, and I add to this regression lead explanatory variables (i.e. future lagged explanatory variables) in ...
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How to interpret differences in VIF and condition number?

In my present data, the Variance Inflation Factors suggest lack of substential multicolliniearity (<1,7). However, the condition number of 28 is almost at the critical value of 30. How do I ...
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Do I have multicollinearity? [duplicate]

I am examining the impact of 7 IVs on one DV using regression anaysis. Some of the IVs are significantly correlated with each other, which is consistent with theory. While the single OLS regression ...
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109 views

How to detect multicollinearity in a logistic regression where all the dependent variables are categorical and binary?

I'm doing a multivariate logistic regression where all my independent variables are categorical and binary. I have transformed all my categorical variables into dummies in order to have reference ...
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Is it possible to check multilevel growth curve models for multicollinearity?

I'm modelling a growth curve model, based on five-time points for n=243 individuals. Time is treated flexible rather than occasion specific. Next to the dependent, continuous variable, I want to ...
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42 views

how to handle very correlated variables

I regress five variables x1-x5 on y using OLS, VECM, ARDL (Im trying to learn all of them). However x1 and x2 are highly correlated and due to multicollinierity the slope coefficients for x1 change ...
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Feature selection based on mean, standard deviation and mean absolute deviation

Suppose we have a large dataset (~ 60000 entries, 58 variables, 4 class labels). For each variable mean, standard deviation and mean absolute deviation are calculated - separately for every class ...
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Can removing predictors increase predictive power in large sample cases?

I am observing an increase in the predictive power of my logistic regression when I remove certain predictors. It might be a bug in my code, so I'm wondering: is this statistically possible? If so, ...