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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what is the difference between collinearity and interaction?

I cannot differentiate clearly between "interaction" and "collinearity" in multiple linear regression. For me these terms are related but not the same. I have searched the forum but could not find ...
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Need to use a percentage as a independent var in regression

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34 views

Principal component (PC) as a substitute for colinear covariates?

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Correlation and collinearity in regression

I did a correlation analysis for my variables. All of them are associated (the coefficient is above 0). However, there is no collinearity problem in my regression analysis. I do not know how to ...
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39 views

logistic regression with dummy variables for fractional factorial design

We have conducted a survey experiment with varying amounts of incentive (factor 1 = I1, I2, I3, I4, I5). The experiment was conducted stepwise in three subsequent studies (factor 2 = S1, S2, S3). ...
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39 views

Highly correlated predictors in backward stepwise regression?

I know that it's not right to enter variables having multicollinearity (high correlation) into a regression analysis. But if I'm using backward stepwise regression could I add all the highly ...
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Problems with calculation of numerical identification w.r.t. ANOVA smooth for large scale matrices

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28 views

Mahalanobis distance in a hierarchical cluster analysis in SPSS

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19 views

Time dummies in panel data — absorbing effects?

I am conducting a data analysis. I have a panel with individual firms with firm-specific and macroeconomic variables. I would like to run an OLS regression adjusted for firm clustering effects and ...
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17 views

GBM, it's overfitting/multicollinearity problem and parameter setting up

I recently came across a predicting problem (0-1 outcome, with more than 80 variables), I decided to use GBM (Gradient Boosting Machine by Friedman)to handle this job. I let the GBM use only 70% of ...
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42 views

Multicollinearity in a “population model”

I was talking with a colleague who told me that at the time of making logistic regression across a population did not have to worry about assumptions such as multicollinearity, because when analyzing ...
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3answers
261 views

Pattern mining on a small data set

I have a small data set 30 features/predictors and 30 observations. My target variable is Oil production and my predictors are well & reservoir properties (depth, trajectory, temperature, pressure ...
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Logistic Regression - Two Dependent variables not playing well with each other

My name is Abhi and I trying to teach myself logistic regression by solving some of the problems available on the internet. I am using R and RStudio as the development environment Problem Statement ...
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32 views

How is predictor importance in a cluster analysis (in SPSS) affected by dichotomy and multicollinearity?

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28 views

Low correlation between predictor variables in linear regression

I know that one if one is trying to perform linear regression, multicollinearity can be an issue because it can "lead to unreliable and unstable estimates of regression coefficients." Suppose for a ...
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24 views

Multicollinearity

In a regression model with dummy variables, how does one check for intearction between the dummy variable and the independent variables?Wouldn't there be problem of multicollinearity when such ...
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68 views

VIF in GLM model in R

Before running or building a model, ho can we check on the multicollinearity between different covariates in GLM model in R? I know that SAS Proc MIXED procedure gives a column for VIF which is very ...
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22 views

Correlated explanatory variables where both are significant

I am running a multiple linear regression using SPSS to test the effect of ethnicity and ethnic/racial attitudes or perceptions on political predispositions. One model, as an example, looks like ...
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14 views

How to obtain VIF values for independent predictors used in a loglinear poisson generalized linear model?

The correlationmatrix shows that some of my predictors are correlating (Pearson, 0,288 0,492 and 0,360) I think it is useful to have additional information to decide whether this is acceptable or not. ...
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50 views

Why does Ridge Regression work well in the presence of multicollinearity?

I am learning about ridge regression and know that ridge regression tends to work better in the presence of multicollinearity. I am wondering why this is true? Either an intuitive answer or a ...
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42 views

Evidence of collinearity, yet significant coefficients – is this a problem? [duplicate]

I have two main explanatory variables of interest in the model I use. It is only when I include them both that the coefficients are significant. The correlation between them is 0.98 and the vif-value ...
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66 views

Multicollinearity with Interaction (high VIF)

When I check the VIF of my independent variables with the dependent variable, it looks normal and less than 5 but when I add the interaction variables, the VIF increase to 48 for some variables. I ...
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54 views

Why is least squares performing as well as ridge regression when there is multicollinearity?

I am learning about ridge regression, so I am implementing it in MATLAB as practice. However, I am having trouble finding a structure of data where ridge regression performs better than an ordinary ...
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perfect collinearity among multiple continuous variables

When there is a perfect collinearity among more than two continuous variables, how do you deal with it and how are the regression results interpreted? I have three independent variables which ...
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Dealing with linear dependent variables

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43 views

Non-linear model robustness

I am building a non-linear model aiming to describe the mechanistic process of resource allocation. There several terms, and what makes the model non-linear is competition between lines that are ...
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136 views

When can we speak of collinearity

In linear models we need to check if a relationship exists among the explanatory variables. If they correlate too much then there is collinearity (i.e., the variables partly explain each other). I am ...
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136 views

Is multicollinearity a concern in nonparametric statistics?

Should I be concerned about multicollinearity in nonparametric statistics?
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Can I use simple regression with variables that are not significant in multiple regression?

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multicollinearity in OLS regression [duplicate]

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88 views

Panel Cointegration, Moderating Effects and Multicollinearity

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31 views

Multicollinearity over intervals

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Is extra sum of squares $SSR(X_p|X_1,…X_{p-1})$ in multiple regression always positive?

An extra sum of squares $SSR(X_p|X_1,...X_{p-1})$, assuming that no pair of predictor variables are perfectly correlated, measures the marginal reduction in the error sum of squares. Eventually one ...
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Is centering a valid solution for multicollinearity?

Let's assume that $y = a + a_1x_1 + a_2x_2 + a_3x_3 + e$ where $x_1$ and $x_2$ both are indexes both range from $0-10$ where $0$ is the minimum and $10$ is the maximum. I found by applying VIF, CI and ...
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How to deal with multicollinearity without dropping variables

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Identifiability in linear regression

If we have a generative model: $X_2=X_1a_1+\varepsilon$, where $\varepsilon \sim \mathcal{N}(0,\sigma_2^2)$, do we have $X_1=X_2a_2+\varepsilon '$, where $\varepsilon \sim \mathcal{N}(0,\sigma_1^2)$ ...
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Why do both the VIF and tolerance statistics exist, when the latter is just the reciprocal of the former?

Is taking the reciprocal helpful in some way, or is it just a matter of historical accident that there came to be two terms to describe the same thing? (From the Wikipedia page.)
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Variance-Covariance matrix interpretation

Assume we have a linear model Model1 and vcov(Model1) gives the following matrix: ...
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62 views

Multicollinearity if independent variables sum to one?

I am intending to explain the variation in dependent variable Y through a number of ratios that, combined, sum to 1 in each period t Hence, $$ Y_t = \alpha + ...
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Variance inflation factor for generalized additive models

In the usual VIF calculation for a linear regression, each independent/explanatory variable $X_j$ is treated as the dependent variable in an ordinary least squares regression. i.e. $$ X_j = \beta_0 + ...
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When including a linear interaction between two continuous predictors, should one generally also include quadratic predictors?

Suppose I am fitting a linear model, and I have two continuous predictors x1 and x2. I think that they might interact, so I add ...
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79 views

Dealing with multicollinearity in logistic regresson model

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Infer causality with high collinearity

I recently started to ask myself how to measure the impact of education on indexes like GDP: what is the outcome of mathematics or computer science on GDP, at the country level for instance. In this ...
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30 views

handling multicollinearity by backwards regression and omitted variable bias

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Techniques for scaling data matrix to avoid rank deficiency issues

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How to deal with imperfect multicollinearity

I am estimating a model for an assignment, and found out that there is a 0.9 correlation between two of the independent variables. So If I am not wrong I should omit one variable and redo the ...
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I have my data set, now what?

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Collinearity in time dummies, fixed effect regression

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