Multicollinearity (in Regression Analysis)
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- Опубликовано: 9 фев 2021
- In a regression analysis, multicollinearity occurs when two or more predictor variables (independent variables) show a high correlation. This leads to the fact that the regression coefficients are unstable and can no longer be interpreted.
To avoid multicollinearity, there must be no linear dependence between the predictors; this is the case, for example, when one variable is the multiple of another variable. In this case, since the variables are perfectly correlated, one variable explains 100% of the other variable and there is no added value in taking both variables in a regression model. If there is no correlation between the independent variables, then there is no multicollinearity.
In reality, a perfect linear correlation hardly ever occurs, which is why we speak of multicollinearity when individual variables are highly correlated with each other, in which case the effect of individual variables cannot be clearly separated from each other.
It should be noted that the regression coefficients can no longer be interpreted in a meaningful way, but the prediction with the regression model is possible.
Multicollinearity test
To find out whether multicollinearity is present, the tolerance of the individual predictors is considered. Another measure of multicollinearity is the VIF (Variance Inflation Factor).
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God bless you woman!
Hi, how do you come up with the critical value of 0.1 respectively 10? Is there a source for that?
Thank you for this wonderful lecture 👍🤝
Thanks for your nice Feedback!
Thank you so much!
Glad it helped!
I am under the Linear regression tab and i do not see the subtab check condition. All i see under Linear Regression is Test assumptions, Effect size and Summary in words. Please help!!! I have a subscription on your website.
Superb, thank you
Many thanks for your Feedback! Regards Hannah
Genius!
Many thanks! Regards Hannah
hi, this video is really helpful. You mentioned in the next video, you will tell how to test the multicollinearity of dummy variables. but I can't find that video. could you send me a link?
Oh sorry, we have just a video on dummy variables! But for dummy variables it is the same, so you test it in the same way. ruclips.net/video/bnjPzHQ04Ac/видео.html
@@datatab ok! Thanks.
Good job
Thanks : )
please provide us a separate video that differentiates between the influence and the prediction? How can I know whether my research is a prediction or influence base? It is confusing.
From your research question? Do you want to predict a variable using one(s) other(s) or do you want to see how much influence one(s) variable(s) has/have on another?
@@leykimayri Hi, thanks for your reply; it is still not clear how to differentiate between them in research? how do I know my research is prediction or influence?
@@alialshebami8408 What do you mean how do you know if your research is prediction or influence? YOU define what research you want to do. You pose your research questions IN ADVANCE and based on those you then design your research (meaning the questions you will ask, who you will ask, for how long your research will be, what kind of questions they will be, how many people, etc etc). Then you analyse your results and based on that analysis you interpret the results and come to conclusions.
Can you make a video about two tailed test for next pls.
two tailed t-Test? or in general two tailed?
Can you make video on coefficient of determination
Thanks for your message! Yes sure! I can try!
Can you teach multicolineality by using this series😢
.what is nature of mulicoliniality
.is multicoliniality really problem?
.what are its practical consequence?
.how do one detectit?
.what remedial measure?
The nature of multcolinarity is the similarity on impact one independant variable has with another independant variable.
it is really a problem because we're looking for a model that best fits, and why have a model that shows 4 independant variables's influence on a dependant variable, when 3 gives same result. Ask yourself is 3 better than 4? yes it is
Practical consequence is that you are not recieving the best model to explain the impact on variables to the independant variable.
you detect it with VIF formula which is (1-(1-R^2) where r^2 is correlation squared or you can get R^2 through running a regression analysis on excel
remedial measure... you remove one of the dependant variables that shows multicollinarity either from a correlation matrix chart or VIF diagnosis, or looking at the P-value regardless of multicollinarity.
You're welcome :)
You can also detect multicollinarit by performing regression model with only the dependant variables, where you have 1 of the dependant variables as y and the rest as x.
this is called auxillary regression
So apparently the service is no longer free.:(
Kindly send video on correlation
Hello Jasbir, just search at youtube "correlation DATAtab" we have greate Videos about correlation!