Tagged Questions

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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Linear regression with redundant features (perfect multicolinearity)

Suppose $X \sim N(0,1)$, $Z=X$, and $Y=X$. An ordinary least squares regression problem is solved: $min_{(b1,b2)} \|Y-(b1*X+b_2*Z)\|_{2}^2$ This is a strictly convex function which must have a ...
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51 views

Multicollinearity in multiple regression

I really hope you can help! I'm in the last stages of my PhD. My supervisor is keen on including all variables in the multiple regressions I am running. Some of the scales are intercorrelated (some ...
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Correlation between offset and predictor in count data

I am fitting a Poisson model that requires an offset term to account for sampling effort. However the offset term is highly correlated with one of the linear predictors that is central to my ...
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Coefficients flip sign in general linear model depending on what predictors are included: collinearity is NOT a problem [duplicate]

I have a general linear model with several predictors (~10). The sign (beta) of one of the predictors (Pred1) is negative when all predictors are included. It's STILL negative when the most correlated ...
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10 views

Combining collinear continuous predictor variables in GBM

I'm dealing with a dataset (n=254) with one dependent variable (Y), and three independent variables (X1, X2, X3), all continuous. I would like to compare the contribution from each IV to Y. I've been ...
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13 views

Centering my variables decreases the condition indices

In my regression model, one of my condition indices is very high, but only the intercept and one of the eplanatory variables have a large variance decomposition proportion for this index. The VIF is ...
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0answers
32 views

Very low VIF values, but extreme high condition index

In my multiple linear regression model, all of my explanatory variables have a VIF score, lower then 3, but the highest condition index is 709. The constant and one of the explanatory variables have ...
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24 views

Regarding analysis of regression result and vif result

I am working on building a regression model. There are 51 points. The number of predictor variables is 37. The following is the result of running lm result. When trying to detecting the ...
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19 views

How Can I Build A Regression Model With Collinear Data?

Hello there my fellow Cross Validated members; I’m here today to brainstorm a little bit with all of you out there, to flesh out our collectively acquired data analytic skills, and to try and find new ...
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23 views

nonsignificant predictor becomes significant

This may be a question with an obvious answer. I am trying to predict change in a continuous variable over time using a linear mixed model in SPSS. There are 4 time points. Ultimately, I would like to ...
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0answers
10 views

Substantial changes in significance level when adding more variables to the model [duplicate]

I have a multiple regression model. When I add one more independent variable to the model the significance level of two of my original independent variables suddenly get insignificant. How come? All ...
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0answers
16 views

Multiple Reg with 2 Independent Variables that are Correlated - Orthogonalizing the IV's

I have two Ind. V's, $x_1$ and $x_2$. They are slightly correlated with eachother. $x_1$ explains a significant portion of $y$'s variability. Rather than just modeling $y = \beta_0 +\beta_1 x_1 ...
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24 views

Multicollinearity, feature selection for discriminant analysis and the error rate

I have a question regarding feature selection in LDA/QDA and deciding to eliminate variables to find an optimal model (lowest misclassification rate) I'm looking at how quadratic and linear ...
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0answers
48 views

Looking for and dealing with collinearity in a GLM

I've got this dataset with one continuous dependent variable and two categorical explanatory variables. I'm wanting to run glms on the data but I'm finding problems with what I think is collinearity. ...
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0answers
20 views

Mean centering variables NOT in a moderation/interaction term [duplicate]

I am trying to assess the impact of multicollinearity in a regression because I have two separately measured variables which have the reversed signs problem (one predictor is +b regression weight, the ...
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1answer
69 views

Multicollinearity among categorical variables - Is it normal?

I'm building a logistic regression model in which almost all of the input variables are categorical. There are multiple sets of categorical variables, for example, day of the week, age range buckets, ...
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3answers
67 views

Too many predictors to manually check linearity

Say I have 1000 potential predictors in a logistic regression. I don't have time to check each predictor manually for linearity. I could wait till after variable selection, but in that case I wonder ...
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15 views

Regression on a Ratio with Numerator and Denominator as regressors

Assume we have a dataset with prices of train-rides. There is the price for the ticket, the distance of the ride und some other relevant variables (e.g. x2: 1st/2nd class, x3: name of train-company ...
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39 views

Is multicollinearity a problem with gradient boosted trees (i.e. GBM)?

A question about multicollinearity for random forests has been asked and answered, but what about boosted trees?
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1answer
42 views

Curse of dimensionality mimics multicollinearity?

Why does the curse of dimensionality mimic multicollinearity, in the following sense.. Consider the random vector $Y = [y_{1}, \dots, y_{4}]$ where each element is ~ Uniform (0,1). Take 10 samples ...
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2answers
101 views

Many dependent variables, few samples: is this an example of “large $p$, small $n$” problem?

"Large $p$, small $n$" typically refers to "many independent variables, few samples". In my case, I have $1$ independent variable, $300$ dependent variables, and $n < 20$ samples. Thus, my case ...
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0answers
13 views

Regularization vs PLS for highly colinear data?

When dealing with colinear data, when would I want to use L1/L2 regularization, and when would I want to use Partial Least Squares? Are there any theoretical or practical reasons to prefer one over ...
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27 views

vif output for multicolinearity when you have factors

using vif(model) in R provides us with the variance inflation factors for out model. often people use sqrt(vif(model)) > 2 ...
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1answer
100 views

How to evaluate collinearity or correlation of predictors in logistic regression?

In linear regression it is possible to render predictors insignificant due to multicollinearity, as discussed in this question: How can a regression be significant yet all predictors be ...
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2answers
118 views

Importance of multiple linear regression assumptions when building predictive regression models

As far as I know, one can differentiate between two main goals of the regression analysis: The goal is understanding causal relations between variables. Here, one has to check several common ...
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0answers
19 views

Multicolinearity: conflicting Spearman rank values and VIF scores

I was wondering if you could provide some advice/feedback on a problem I have been having with detecting multi-colinearity. First, the variables which I am using are not normally distributed, hence I ...
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1answer
20 views

Why do we use fixed effects and controls at the same time?

When we run a regression model (say OLS for simplicity) of y ~ x, we might have to use several control variables say z1 and z2. Now our model is y ~ x + z1 + z2, we may believe that z1 and z2 are not ...
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18 views

Concern about collinearity when adding gender and gender-specific comorbidity for prediction of disease risk

I am build a model to predict the risk of having disease X, let say I have a series of variables and I select the variables to be included in multivariate logistic regression model by: i) clinical ...
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1answer
137 views

Assessing the Contribution of each Predictor in Linear Regression

Say I build a linear regression model to identify linear dependencies between variables in my data. Some of these variables are categorical variables. If I want to evaluate the contribution of a ...
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1answer
183 views

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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36 views

Need to use a percentage as a independent var in regression

This is my first question! My firm takes boxes of documents, preps them (removing staples, taping torn documents), and then scans the documents. I want to calculate what effect staples and torn ...
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1answer
41 views

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

I am working on a spatial linear regression and I can tell there is collinearity between covariates. Can I use PCA (Principal Component Analysis) images instead of original covariates to estimate the ...
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2answers
75 views

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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140 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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1answer
62 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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0answers
18 views

Problems with calculation of numerical identification w.r.t. ANOVA smooth for large scale matrices

Suppose we have two (centered) Spline-matrices $\boldsymbol{B_1}$, $\boldsymbol{B_1}$. Then $\boldsymbol{X_1} = [\boldsymbol{B_1},\boldsymbol{B_2}]$ contrains lower order smooths and ...
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1answer
153 views

Mahalanobis distance in a hierarchical cluster analysis in SPSS

I am conducting a hierarchical cluster analysis in SPSS on my database with several neuropsychological and psychiatric variables. In my database, some of my variables (that is: two pairs of variables) ...
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1answer
52 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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0answers
49 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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0answers
47 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
299 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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0answers
34 views

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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0answers
70 views

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

I want to use a cluster analysis (CA) in SPSS to define different profiles in my dataset. I am using different continuous variables for this, including several neuropsychiatric measures. I am new in ...
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1answer
44 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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0answers
36 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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0answers
215 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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1answer
23 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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0answers
23 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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1answer
60 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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0answers
43 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 ...