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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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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23 views

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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1answer
27 views

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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12 views

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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8 views

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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1answer
19 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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6 views

Forecasting techniques for appliance industry [closed]

How to calculate sales forecast in push environment? Appliance industry. What are the independent variables/factors i have to consider?
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50 views

Will PCA also produce multicollinearity? [closed]

It's well known that PCA will generate orthogonal basis. But in practice, I found that even after PCA, I can still face the problem of multicollinearity. Is it due to the numerical limit or I did ...
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13 views

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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18 views

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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2answers
73 views

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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1answer
342 views

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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18 views

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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1answer
28 views

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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18 views

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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14 views

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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1answer
42 views

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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2answers
50 views

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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22 views

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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32 views

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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19 views

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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1answer
56 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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11 views

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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37 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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1answer
25 views

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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53 views

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, ...
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1answer
31 views

Is it correct to use sampling weights for reducing multicollinearity in Probit?

I tried to estimate a probit model: $$y=b_1x_1+b_2x_2+b_3x_3,$$ where all variables are string and $x_1$ and $x_2$ are correlated. $x_1$ means GINI index and $x_2 = \text{self-employment rate}\cdot ...
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32 views

Why do t statistics decrease (standard error for cofficient increases) when multicollinearity exists?

Can anybody show how the t statistic decreases when multicollinearity exists? It is easy to prove using the F test, but I don't know how to prove it using the t test.
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1answer
33 views

Different coefficient values from multiple versus bivariate regression

I wonder how to generate such data, so that in single variable regression feature coefficient would be positive, and in multiple regression would be negative. So I read several related questions on ...
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1answer
981 views

Why does this regression NOT fail due to perfect multicollinearity, although one variable is a linear combination of others?

Today, I was playing around with a small dataset and performed a simple OLS regression which I expected to fail due to perfect multicollinearity. However, it didn't. This implies that my ...
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35 views

Vif and stepwise regression

I used the VIF to detect multicollinearity, I want to use forward selection and backward elimination procedures. My question is: Do I have to use all the variables in my dataset in the procedures or ...
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12 views

reconciling Linearity and Multicollinearity assumptions in ANCOVA

For ANCOVA, many textbooks and other resources require, among other assumptions, that covariates be linearly related to the dependent variable, which makes sense. However, many of the same sources ...
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31 views

Issue with linear mixed effects model and interaction : any alternate method?

I have a variable (Y) which I'd like to know if it can be explained by linear regression with two other variables (A and B) or the interaction of A and B. Let's say, to simplify, than I have two ...
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50 views

Is it appropriate to test for collinearity in a mixed model using VIF?

My study is examining predictors of skin lesions in pigs. I am looking at the effect of predictor variables (including weight at 4 weeks, 9 weeks and 20 weeks) and I have carried out a mixed model ...
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Is the intercept estimation affected by multicollinearity?

Suppose I am running a regression $$x_t = \alpha + b_1y_{1t} + \dots + b_m y_{mt} + \varepsilon_t$$ where the $y_{i}$ are potentially linearly correlated (Some have an IVF bigger than 4; generally ...
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41 views

Train & predict probabilities using LDA having multiple collinearities

I am trying to fit an LDA model and predict conditional probabilities of class membership with it. I believe I understand the basic method to do this using the covariance matrix and class means, but ...
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1answer
72 views

Adding new variables makes regression coefficients individually insignificant [duplicate]

I have a multiple regression where all my coefficients are significant. When I add new variables my initial variables become insignificant. Furthermore, my new variables (that in a simple regression ...
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1answer
48 views

Regression — multicolinearity and VIFs

I understand that variance inflation factors can be used to detect multicollinearity. What is the intuition behind the VIF formulation? What aspect of this formula shows it detects multicollinearity ...
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12 views

Nonlinear least squares for multicollinear data - regularization etc?

Is there an existing technique that can be used to fit NLLS on multicollinear data? Such as ridge regression for NLLS, or any other technique used to solve the same problem? My model takes the form ...
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1answer
45 views

Does multicollinearity affect performance of a classifier?

I know that wikipedia references are sometimes frowned upon here, but this one has me puzzled: Wikipedia - Multicollinearity I know what multicollinearity is, and ...
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74 views

Alternatives to Box-Tidwell transformation for ridge regression?

I would like to fit the following model by ridge regression (the xs correlate strongly with one another) $y = \beta_1 {x_1}^{\lambda_1} + \beta_2 {x_2}^{\lambda_2} + \beta_3 {x_3}^{\lambda_3} + ...
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1answer
145 views

how does multicollinearity affect feature importances in random forest classifier?

I have a random forest binary classifier, but the results from the feature importances are somewhat erratic. Here's what I want to know: Does multicollinearity ...
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35 views

Correlation between two explanatory variables

I have a multiple regression. Two of my independent variables are "Repeated Partnerships" and "Company size". When adding the explanatory variable "Company size" to the regression it is statistically ...
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3answers
215 views

Lasso Regression for predicting Continuous Variable + Variable Selection?

I'm attempting to predict vegetation productivity based on climatic and land use variables (the latter are categorical). I found that there is a multicollinearity problem between the predictors ...
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1answer
121 views

Multicollinearity in simple linear regression (not multiple)?

I am doing a simple linear regression analysis with 1 independent variable. I am checking data against assumptions. As I am checking against Tolerance and VIF level, I get the their values equal to 1 ...
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54 views

Multicollinearity problem and differencing time series

I have to estimate the regression equation by OLS and do in-sample forecasting of the time series. It has a trend and seasonal variations. So I try to estimate the model which looks like $$\text{ln} ...
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62 views

model estimation with feedback loop in variables

I have the following problem regarding fitting of a model where some variables can be biased so that their values are based on knowledge or at good guestimate of $Y$ in the past. In other words: some ...
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2answers
93 views

Choosing predictors in regression analysis and multicollinearity

I would like to run a linear regression analysis and I'm uncertain about including predictors. I have three predictor variables available. One is based on a lot of previous research. Therefore I am ...
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1answer
48 views

How to solve multicollinearity in OLS regression with correlated dummy variables and collinear continuous variables?

I have predicted an ecological variable using OLS regression which showed the model accounts for more than 72% of the variance in the dependent variable (DV). However, I am also interested in which ...