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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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Interactions: mean centering, standardizing and standardized coefficients (betas)

I mean-center my independent and moderator variable before calculating the interaction term to avoid multicollinearity. In my regression output table, I subsequently report the standardized ...
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25 views

Controlling for variables in social sciences

I know this is a completely hypothetical scenario but I just want to understand how the effect of a variable could be held constant and how the coefficients of two independent variables are estimated ...
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54 views

Multicollinearity in simple linear regression

If there's perfect or near multicollinearity problem in a simple linear regression $y_i = a + b x_i + u_i$, what characteristics does $x_i$ have? I think if there's perfect multicollinearity, it ...
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30 views

Keeping two correlated variables in the model

I am using OLS: In my model I have two variable (X1 & X2) which are correlated (correlation = 0.47). My prediction is that X1 should be negatively associated with Y and X2 should be positively ...
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Does collinearity of two features affect the predictive performance of support vector classifier?

I have a set of features for my machine learning model (support vector classifier, SVC), two of which are strongly positively correlated (i.e., diameter and spherical volume). Does this affect the ...
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22 views

Collinearity among OLS regressors may inflate their variance, but can it also change their estimated value?

I've read that collinearity between independent variables in an OLS regression may inflate the variance of the OLS estimates. But, can it also change their estimated value?
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Collinearity when regressing against three sets of dummies

I would like to regress price of food products against three sets of dummy variables: 1. the food product itself (13 products) 2. the country where the food product was priced (119 countries) 3. the ...
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How to improve on the preferred regression model?

After running the chow test, the F stat shows structural change is present in the model, so the unrestricted models are preferred. I am not sure how I can choose from the two regression models in the ...
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How to assess whether the couples are significantly more correlated with their partners than other study participants?

I am looking for statistic advise. I have a small sample (86 participants) of which 30 are husband and wife (15 couples) and the rest are either single or have a partner but the partner is not in the ...
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24 views

Interpreting dummy variable interaction terms

I am attempting to model monthly retail electricity sales. To account for both the effects of seasonality and weather, I created an interaction term by multiplying 12 monthly dummy variables by the ...
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27 views

Interactions terms and the dummy variables

I am attempting to model monthly retail electricity sales. To account for both the effects of seasonality and weather, I created an interaction term by multiplying 12 monthly dummy variables by the ...
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1answer
25 views

Is “$\mathrm E[X'X]$ has rank $k$” the assumption of no multicollinearity?

My lecturer wrote this on the board: Assume $\mathrm E[X'X]=Q$ has rank $k$, where $X$ is the data matrix and $k$ is the number of independent variables. I asked her if that is the assumption of ...
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How is GVIF calculated for categorical variables?Also is there any other way to detect multi co-linearity of categorical variables?

I was tring to find a way to remove the redundant categorical variables as features. I believe GVIF would give high value for the redundant/multicollinear categorical variables. Please let me know if ...
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Interpretation of removed continuous variables in regression due to linear dependence

I have created a standard OLS regression model to estimate the House Price and one group of variables describe the age group percentage of population in a particular neighborhood (ranging 0 to 100). ...
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Including or excluding a variable and what about the p-values when VIF = 5.2?

I'm writing my master thesis and run into a question about multicollinearity. I have two interaction effects which have a high VIF (5.2, 4.8). Both are interaction effects between categorical ...
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How do I classify multiple time series into different buckets?

I have 60 different time series, denoted $\{y_{i,t}\}_{i=1}^{60}$. So $i$ denotes the time series in question, and $t$ obviously denotes the time period from $t=1$ to $t=T$, where $t \in \mathbb{N}_{+}...
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R - checking colinearity of 3 categorial and 1 continuous variables

I have the following variables which are expected to influence the dependent variable kg waste: turnover (continuous), restaurant type (either D or I), operation (either P or N), owner (either M or F)...
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2answers
69 views

Choosing model for more predictors than observations

I'm working with a data consisting of 1000 observations of 2000 predictors and one variable we wish to predict. There are couple of problems I can't get around. I am aware that such setting has been ...
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45 views

How to overcome Coefficients: (4 not defined because of singularities) [duplicate]

Stats is not my strong point but trying to run a regression. I'm aware that it happens because some of these variables are perfectly collinear. However, I do not know how to fix this? Any help would ...
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Partial Least squares regression - Variable Importance on Projection (VIP) method of selecting variables

I understand that partial least squares regression produces VIP scores for each predictor variable enabling variable selection (using a VIP threshold of >1). Does this method account for collinearity ...
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Is it possible to resolve collinearity between a categorical and a continuous variable using a random effects model?

This question is related to Multicollinearity between categorical and continuous variable. Consider a regression model x ~ y + z + ... + w with outcome x and ...
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40 views

Do control variables in a regression analysis cause collinearity?

This is something that bothers me for quite some time, but I didn't find yet a satisfactory answer. I hope that the wisdom of the people hear will help me to clarify this: In a multivariate ...
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38 views

Application of GAM on large dataset

I was suggested that my questions were too broad. As I commented below, I have nearly a million data points and perhaps a hundred variables. This may be a very basic modeling question: I am curious to ...
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Can I include an 'industry dummy' in my Relative Weight analysis? Bootstrapping produces error message!

I am analyzing the impact of 7 different employee satisfaction variables(x1, x2,...,x7) on financial performance in 200 companies over 3 years. Since these 7 predictor variables are highly correlated ...
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Negative coefficient in model caused by weak multicollinearity

I performed multiple regression and obtained the following model: Q = (0.33495)P + (-76.89321)G + (6.31424)P・G - 3.36334 P = precipitation; G = groundwater; Q = stream discharge The coefficient of (...
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Linear regression and multicollinearity

There is a multiple linear regression model being created. Y=ax1+bx2+cx3 Following HYPOTHESES are formed Variable x does not impact y for all variables x1, x2 , x3 and so on. We removed a ...
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111 views

Which multivariate test on repeated k-fold cross-validation with collinearity?

I am running repeated k-fold cross validation (5x5-fold) for comparing two models based on 3 dependent numerical variables (X, Y, Z) and 4 independent categorical variables (A (two groups), B (five ...
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35 views

Regression when the independent variables are counts by age group

I want to run a regression where the independent variables are counts of number of people in different age buckets (size 10 e.g 25-34, 35-44 etc...) and the objective is to understand the effect of ...
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1answer
29 views

Independent variable collinear with intercept

I have high collinearity between one of the covariates (credit score variable with values ranging from 600 to 800) and the intercept term, when regressing a continuous dependent variable on some ...
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15 views

Multicollinearity between categorical and continuous predictors [duplicate]

How can I check collinearity between categorical and continuous predictors (there are four predictors)?
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1answer
22 views

Year of intervention seems a better predictor than type of intervention (which is dependent on the year itself)

We have a small(n = 19) non-randomized pilot clinical study in which we compare two types of surgical procedures on various outcomes. The choice of which procedure was to be performed depended ...
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13 views

Proof for multicollinearity consequence

I came across this statment "Even extreme multicollinearity (so long as it is not perfect) does not violate OLS assumptions. OLS estimates are still unbiased and BLUE (Best Linear Unbiased Estimators)"...
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Interpreting $p$-values and Chi square when variables are added to a model in maximum likelihood estimation

I'm running a maximum likelihood estimation (probit) and I'm experimenting with adding additional variables, walking the bias-collinearity tightrope. Please could someone explain to me intuitively ...
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1answer
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solutions for muticolinearity

how do you linearly combine predictors, such as adding them together to avoid multicollinearity? I have multicolinearity in my regression. say the function is lm(y ~ x + z) where x and z have ...
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1answer
31 views

Is repeated propensity score matching over many 0-1-features a valid procedure?

I would like to do a simple linear model where the outcome $y$ is real-valued, but my data matrix $X$ consists of several hundred features that all are $0$-$1$-valued. The number of observations $n$ ...
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2answers
25 views

Estimating effect of linear regression coefficients with multicollinearity

As I didn't find a satisfying for that questions I try it here: I have a multivariate Lineare Regression model with some correlated predictor variables. The "simple" question I want to answer is: "If ...
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Can a complex interaction term mean more than what it's composed of?

I'm cross-posting this question on both Economics and Cross Validated to get answers from a different perspective on each field. It is generally accepted to cross-post if the question is tailored to ...
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1answer
41 views

multicollinearity between confounders logistic regression

Im going to investigate if a disease have an negative impact on a binary response variable. The disease is the independent variable with additionally confounders. I want to do a manual stepwise ...
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1answer
42 views

Non-significant in correlation, but significant predictor in regression. How to explain suppression?

I'm having trouble explaining some results... I have 5 independent variables (A, B, C, D and E) and I want to know their relation to the dependent (Y). Only variables A and C are significantly ...
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174 views

Can i include the product of two random variables? Or do I risk collinearity?

I have a model in which I want to predict Y. My regressors X, are x1 and x2. For some reason I believe that it would also be useful to include into the model: ...
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1answer
63 views

Construction of linear mixed model (using R)

I would like to use Lineal Mixed model to see if the treatments I applied to some soil changed significantly their CO2 fluxes. I have 2 temperature (t1, t2) and 3 inundation (w0,w1,w2), resulting in ...
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46 views

Doesn't high feature correlation decrease random forest accuracy?

I have generated a dataset of artificial data and want to distinguish two labels from each other using a random forest. I thought having correlated features in my dataset will decrease the algorithms ...
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2answers
82 views

Why is my variable being omitted by stata?

I am carrying out a fixed effect regression. I have a dummy variable called female. The dependent variable docvis refers to hospital visits. I created an interaction term between hhkids and female ...
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1answer
39 views

Use Residuals to remove multicollinearity

This is probably a matter of me not knowing the terminology, but suppose I want to isolate the effect of $X$ on $Y$, and I have some other factor $Z$ that is somewhat correlated with $X$. So the ...
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29 views

How to estimate the VIF for geeglm models in r

I am very new in r and at analyzing gee models. I have a very high dimensional data (51 independent variables measure at multiple times with no missing values (secondary dataset)). I am pretty sure ...
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14 views

OLS Multicolleniarity

I have a pretty simple task to estimate ols multiple regression. I need a measure of multicolleniarity. Is condition number a good measure and what criteria exists fot its value?
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186 views

Error in vif.default(glm.fit1) : there are aliased coefficients in the model

I have 12,000 records and I'd like to predict a two-class outcome. The dataset has 3 numeric predictors and twenty categoric predictors. The problem is that I have perfect collinearity somewhere ...
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44 views

How do I diagnose collinearity with rfe() from the caret package?

I have 12,000 records and I"d like to predict a two-class outcome. I'm deciding which predictors to keep and I'm having trouble with two problems. 1- I get an error message because I have categories ...
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1answer
34 views

Create composite variables using items with different scales- dealing with multicollinearity

I have IV's that are highly correlated with one another. The first set of correlated IV's, I combined them by adding the score and dividing by 2 to create a composite score. This was simple because ...
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112 views

Interpreting interaction term on highly correlated variables

Somebody at a meeting today made the following comment about a Marketing Mix Model (Linear Regression) we run every year. We should account for the high collinearity of the two Marketing Variables (...