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Questions tagged [multicollinearity]

Situation when there is strong linear relationship among predictor variables, so that their correlation matrix becomes (almost) singular. This "ill condition" makes it hard to determine the unique role each of the predictors is playing: estimation problems arise and standard errors are increased. Bivariately very high correlated predictors are one example of multicollinearity.

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Endogeneity versus multicolinearity in regressions?

(I limit myself to two explanatory variables to keep it simple) So as I understand it, when an explanatory variable is highly correlated with another variable then it becomes difficult to distinguish ...
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Omitted Variable Bias & Multicollinearity: Why are the coefficient SEs smaller in the unbiased specification?

In Introductory Econometrics: A Modern Approach, Wooldridge writes the following regarding the omitted variable bias and its effect on the variance of the OLS estimator (x1 and x2 are correlated): ...
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Assess collinearity in conditional logistic regression [closed]

I have a cross-over design dataset (each patient has two observations - one for case interval; the other one for control interval). All the interval-level variables (exposure and other covariates) are ...
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Dealing up with collinearity predictors' choice in xreg using auto.arima

I'm trying to do a regression with arima errors in R, with xreg in auto.arima following https://otexts.com/fpp2/ by https://robjhyndman.com/ but I have some questions about the predictors' choice in ...
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Is it possible to use an ensemble of regression predictions to avoid issues of multicolinearity?

I am using a regression approach to make predictions using a variety of variables. However, some of my variables are pretty collinear (with a Pearson's r > 0.75), so I can't include them all in the ...
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Understanding VIF procedure with different methods from r package rms and car - VIF by level of a factor

Consider the output from two different r packages, rms::vif and car::vif: ...
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5answers
119 views

How do I handle terms with collinearity?

I'm working on a regression model with the Hitters data from the ISLR package. It has ~300 observations and 20 variables. I want to predict a player's salary. I have major problems of collinearity ...
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I am making a logistic regression model. Should I test for multicollinearity in dependent features if my predicting feature in categorical?

I have a doubt, will multicollinearity affect my Logistic Regression model as my predicting(output) feature is categorical? (because correlation will make sense only for 2 continuous features and not ...
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how can I interpret a long-run covariance matrix?

can I use the long-run covariance matrix to examine multicollinearity between my independent variables? and if yes, what how can I interpret matrix for determining Multicollinearity?
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What is the meaning of correlations between regression coefficients?

I understand that coefficient correlations can arise in the presence of correlated covariates, essentially indicating that our inference for those parameters is coming from information that cannot be ...
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vif range in multi linear regression model

Explain the interpretation of the result of vif .I have 3 independent variables in a multi regression model and the all the three vif values are less than 3.What interepretations I can draw from the ...
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multicollinarity issue

I ran a logistic regression moderation analyses and I noticed that with the addition of the interaction term, one of the predictors flipped signs. The variable (X) was previously -.015 and became .006 ...
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Uniqueness of partial covariance/corrlation if OLS is not unique

Let $X,Y,Z=(Z_1,...,Z_n)$ be random variables. Define the partial covariance between $X$ and $Y$ given $Z$ as: $$\rho_{X,Y \cdot Z} := cov(\hat{X}-X, \hat{Y}-Y)$$ where $\hat{X}$ and $ \hat{Y}$ are ...
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Interpretation of parameter stability of regression coefficients and stability of predictions when multicollinearity is present

It is known that when the predictor variables are highly correlated with each other (e.g, correlation coefficient is 0.9) the regression coefficients are unstable as they have high standard errors. I ...
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Why is the value of vif from $(X'X)^{-1}$ not matching the result?

The diagonal elements of matrix $C = (X'X)^{-1}$ are $C_{jj} = \frac{1}{1-R_{j}^{2}}$ (which is nothing but the vif) of $x_j$ where $j = 1, 2, 3, ..., n$ and $X$ is a $n\times p$ matrix and $R_j^2$ ...
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Does standard error affected by the coefficient?

I make a comparison on ridge regression and OLS using simulation. As i set my correlation as 0.9, which is high, i expect the standard error of ridge regression to be low. However, it is not. ...
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Cox proportional hazards model, problem with correlated predictors or overfitting?

I have a question concerning a Cox proportional hazard model (in R) on which I would love to get your opinion and feed back! I think that there is a problem, however I would like to be sure and ...
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Softmax regression vector features?

I'm working on a difficult classification problem where there are a very large number of known classes (~50k). I have only about 20k labelled points, but these only represent <1% of the possible ...
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R code for robust ridge regression

I am having trouble in searching for the MSE value in using robust ridge regression. The robust estimators that i used is LTS and MM. However, when both robust estimators were applied to ridge, the ...
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Incorporating prior knowledge into feature selection in the setting of multicollinearity?

Background: I'm trying to find the optimal combination of two parameters for finding the first peak meeting some criteria in a signal. The filtering is a bit simplistic, there's a threshold (...
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389 views

Explaining multicollinearity in layman's terms

Say we have a study where we want to run a logistic regression on a group of people, and we want to find out whether one attribute of a person makes them more likely to be a smoker. So we have smoker ...
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How does Stata test for collinearity? [duplicate]

My question is what stata is doing behind the scenes to detect collinearity. I previously assumed that it simply checked to see if there was a strict linear relationship between two variables, as in: ...
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Is this a valid work-around for collinearity?

A fellow PhD student has monthly data on temperatures (T) and precipitation levels (P) for a certain agricultural region. He would like to use it to predict total farm revenues (Y) for year t: $Y_t=\...
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Linear Regression on Boston Housing Price? [duplicate]

As far my knowledge, Linear Regression assumes that data or columns are normally distributed and doesn’t have multicollinearity amongs the features, But when I apply Shapiro test, it shows that none ...
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1answer
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How can I use linear/logistic regression for inference with colinear variables and a smallish dataset?

I have a dataset of around 120 observations, with 30 calculated variables and I am trying to predict a continuous response (result of an experiment) using those 30 variables. Ideally the smallest ...
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Steps after calculating VIF to deal with multi collinearity

How do we eliminate variables after calculating VIF ? I have read about step wise removal of variables with high VIF till we reach VIF values below 5. On the other hand , my professor told me that ...
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Transforming panel data OLS into cross-sectional data model

I am currently stuck on a task where I am interested in estimating the production function for agricultural output using panel data as follows: \begin{equation} y_{it} = x_{it}\beta + \alpha_i + \...
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Consistent estimate of $\beta$ in linear regression under multicollinearity

I am currently stuck on a task where I am interested in estimating the production function for agricultural output as follows: \begin{equation} y_{i} = x_{i}\beta + \alpha_i + \epsilon_{i} \end{...
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How Can I Test for Multicollinearity in my Categorical Predictor Variables when Doing Ordered Logistic Regression?

I have a dataset where my dependent variable measures 'How much Trump’s locker room video should have mattered in the election'. The categories are coded between 1-5, where 1 represents 'should not ...
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collionarity diagonistics

from sas output I got two tables for collionarity diagnostics. Collinearity Diagnostics and Collinearity Diagnostics with intercept adjusted. Which table should I interpret for eigen values eigen ...
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Is it sufficient to tackle collinearity by correlation matrix? [duplicate]

I am doing a regression project with my groupmates. The response variable is house price and the regressors are mixture of numeric, ordinal and categorical variables. We come upon collinearity issue. ...
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3answers
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Does multicollinearity cause type I errors?

Citing Wikipedia on multicollinearity, One of the features of multicollinearity is that the standard errors of the affected coefficients tend to be large. In that case, the test of the hypothesis ...
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evaluate relationship between 1 independent variable and multiple dependent variables.

To evaluate the relationship between variables coefficient of correlation and coefficient of determination obtained from regression are used. But what is the procedure if I have 1 Independent variable ...
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38 views

Concurvity in additive modelling. Parametric term?

I am modelling a outcome in gam were two variables (x1 and x2) are continuous and the other four are factors. I have a suspicion that x1 and x2 might be collinear so I want to check that. As I ...
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Multicollinearity - doubt on regression coefficients [duplicate]

Why is it the case that when there is multicollinearity, the regression coefficients become indeterminate and their standard errors become infinite?
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1answer
42 views

Is there a collinearity issue when using: x, dummy indicating extreme negative value of x and their interaction?

I was wondering whether I can build my baseline model using the following variables without incurring in any multicollinearity issue: $X_1$= Net capital flows over GDP (which may be positive and ...
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VIF - Variance Inflation, when to remove the variable

I'm doing a regression analysis on cement mixtures. The goal is obviously to create the mixture with the most strength. Here are the following variables for me to work with: Variables: Strength = ...
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1answer
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What does “regression of predictor onto all of the other predictors” mean?

I encountered a lot of references that talk about R squared but I can't understand what the difference is between the R squared in regression of the response on the predictors and the R squared that ...
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Including transformations of the same predictor in a multiple linear regression - inflated variances?

I'm working through Introduction to Statistical Learning and in chapter 3 (Linear Regression), I learn that if a relationship between predictors and a response is not linear, I may use cubic ...
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1answer
57 views

Statistical significance testing for multiple categorical variables: modeling coefficient of variation of temperature inside houses

I am a beginner in R (and stats), so please excuse the simple nature of my question. I have a range of variables all relating to the physical and social characteristics of households in the UK. I am ...
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Is multicolinearity testing altogether useless if the p-value on each regressor is less than 0.01?

Isn't the effect of multicolinearity to artificially increase the standard errors and thus artificially decrease the t-statistics, and thus artificially increase the p-values? If so, if all ...
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1answer
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How to use Design of Experiment (DoE) to reduce the number of simulations?

I am planning to do simulation for parametric study and there are 9 parameters in total. I was suggested to use DoE to reduce the number of simulations that I need to do. I studied the basic of DoE ...
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85 views

Inference from regression in presence of multicolinearity

I would like to estimate the effect of one independent variable on the predicted variable in the purely observational study. On the other hand I know that there exists another independent variable, ...
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17 views

Are these two variables collinear in regression?

I have a dummy variable equal to 1 every year of road access, and another dummy variable equal to 1 in the year connection started. Can I include these two variables together as explanatory variables?...
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Exploratory factor analysis of a non-normally distributed data set with probable multicollinearity problems

I am trying to develop a scale measuring employee satisfaction regarding organizational support using spss. Most of my variables are positively skewed which should not be a problem in EFA as long as ...
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1answer
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If x4 has high negative correlation with x1 - x3, which all correlate highly with each other, will x4 also be difficult to estimate in a regression?

Say you have predictor variables x1 through x4 and response y. x1, x2, x3 all highly correlate with each other, thus they will have inflated standard errors due to that multicollinearity. Meanwhile x4 ...
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1answer
41 views

What to do if recombination of independent variables cause multicollinearity issue?

Let's say you use a regression that has either: 1) interaction variables or 2) polynomials. When using those features you may run into multicollinearity issues. Do you know how to resolve this issue?...
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160 views

Can we use covariance matrix to examine feature collinearity?

Consider using Multi-variate Gaussian to approximate $X = [X_1, X_2, ..., X_n]$ and $X_i = [x_{i1}, x_{i2}, x_{i3}, ..., x_{im}]$, so we have n data points and each data point has m features. Multi-...
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What regression diagnostics should I perform for an ordered probit?

Currently I have done the following diagnostics with the linktest multicollinearity with vif the parallel lines assumption with lr test of the oprobit and goprobit. I have seen that I may have to ...
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2answers
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ANOVA multicollinearity adjustment

I am using the statsmodel.ols module to compute an omnibus (ANOVA) F-test for three within-subjects factors; 2*3*2 levels design. The Cond. No. of the omnibus test (...