<p>For Bivariate Regression</p>

<p>Your model would be :</p>

<p>IQ = B0 + B1(Your Education) </p>

<p>For Multiple Regression: </p>

<p>You would use a correlation matrix/plot to do this. Basically it would give you a matrix with correlation values of all variables to each other 0 being none and 1 being perfect. This will also allow you to see the multicollinearity problem which is when independent variables are related to each other. </p>

<p>You can do this in R or Stata. I can post the R script of a similar regression I have done if you would like. </p>

CC BY-SA 4.0
2019-12-22T01:15:38.140Z
How does this answer the question? How do you 'see' the multicollinearity problem from the correlation matrix?
Brennan
CC BY-SA 4.0
2019-12-27T01:54:33.143Z
The correlation will be near to unity if there is multicollinearity between two independent variables. This is usually the first form of detection used and is a solid indicator.
Rumi
CC BY-SA 4.0
2019-12-27T19:38:28.323Z
I would say its one way but by no means is it sufficient, whats the cutoff for ‘near unity’? What if it is 0.8? I think you should include your above comment in the answer as OP may not be familiar with what to look for, all you say is you can ‘see it’. I think there is too much ambiguity using this measure alone. I would include VIF’s in your answer.
Brennan
CC BY-SA 4.0
2019-12-27T22:15:12.797Z
I certainly agree to some extent with what have you said. Obviously what I had stated is what you would look at initially. If you are not satisfied you would go about using the VIF way or Eigensystem Analysis. I guess I should have included that in my answer.
Rumi
CC BY-SA 4.0
2019-12-28T01:07:00.867Z
Yeah, you should edit your answer and include it, it would make for a comprehensive answer to the more general question
Brennan
CC BY-SA 4.0
2019-11-21T22:32:41.513Z
2019-11-21T22:32:41.513Z
Rumi
<p>I have a regression model that includes IQ test scores as the dependent variable; my own education, my father's education and my mother's education as independent variables. Suppose I want to know whether the only way parents'education increases my IQ test score is through my own education. How would I test that hypothesis? </p>

CC BY-SA 4.0
2019-11-18T02:36:19.023Z
So if you think about this, then according to your hypothesis if we ran the regression of IQ test score on parents education then parents education should be significant. And if we regess IQ test score on own education then own education should be statistically significant. However, if we regress IQ test score on own education and parents education then parents education would be insignificant and own education would be significant since own education contains additional information than parents. This assumes that the model is correct and that there isn't something important that is missing.
Andrew M
CC BY-SA 4.0
2022-12-06T12:13:35.123Z
I can't give an answer, I don't know it well enough, but I suggest you read into mediation analysis. This is perhaps a good starting point: https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12452
Papayapap
<econometrics><mathematical-economics><labor-economics>
How to test if the effect of one regressor entirely comes from other regressors?
Alex Wang
# How to test if the effect of one regressor entirely comes from other regressors?

(Asked by: username_0 on 2019-11-17)

I have a regression model that includes IQ test scores as the dependent variable; my own education, my father's education and my mother's education as independent variables. Suppose I want to know whether the only way parents'education increases my IQ test score is through my own education. How would I test that hypothesis?

**Tags:** econometrics, mathematical-economics, labor-economics

> **Comments:**
> **username_1**: So if you think about this, then according to your hypothesis if we ran the regression of IQ test score on parents education then parents education should be significant. And if we regess IQ test score on own education then own education should be statistically significant. However, if we regress IQ test score on own education and parents education then parents education would be insignificant and own education would be significant since own education contains additional information than parents. This assumes that the model is correct and that there isn't something important that is missing.  
> **username_2**: I can't give an answer, I don't know it well enough, but I suggest you read into mediation analysis. This is perhaps a good starting point: https://onlinelibrary.wiley.com/doi/full/10.1111/joes.12452  

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## 1 answers

### Answer by username_3 on 2019-11-21. Score: 0

For Bivariate Regression

Your model would be :

IQ = B0 + B1(Your Education)

For Multiple Regression:

You would use a correlation matrix/plot to do this. Basically it would give you a matrix with correlation values of all variables to each other 0 being none and 1 being perfect. This will also allow you to see the multicollinearity problem which is when independent variables are related to each other.

You can do this in R or Stata. I can post the R script of a similar regression I have done if you would like.

> **Comments:**
> **username_4**: How does this answer the question? How do you 'see' the multicollinearity problem from the correlation matrix?  
> **username_3**: The correlation will be near to unity if there is multicollinearity between two independent variables. This is usually the first form of detection used and is a solid indicator.  
> **username_4**: I would say its one way but by no means is it sufficient, whats the cutoff for ‘near unity’? What if it is 0.8? I think you should include your above comment in the answer as OP may not be familiar with what to look for, all you say is you can ‘see it’. I think there is too much ambiguity using this measure alone. I would include VIF’s in your answer.  
> **username_3**: I certainly agree to some extent with what have you said. Obviously what I had stated is what you would look at initially. If you are not satisfied you would go about using the VIF way or Eigensystem Analysis. I guess I should have included that in my answer.  
> **username_4**: Yeah, you should edit your answer and include it, it would make for a comprehensive answer to the more general question  

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