- Thread starter LoulouVille
- Start date
- Tags r-square regression

Hello! Thank you for replying. Im an undergrad writing for thesis. The topic is about human capital. The variables included are GDP per capita being the dependent var, life expectancy completion rate of higher ed and techvoc being the independent var from 2000 to 2018. We are testing their relationship. the R² is 0.996134. It was my thesis adviser who told me that this is too high. She said that only 0.1% of unexplained variability is impossible. she is deadset too in making the R² lower.

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Ok, what does she suggest as the reason for this extremely large R², and what does she suggest as remedy against this?

You did not report your sample size (important), number of variables (important), and what the model looks like (important).

Please explain your research question, your regression model, number of variables, sample size.

With kind regards

Karabiner

You did not report your sample size (important), number of variables (important), and what the model looks like (important).

Please explain your research question, your regression model, number of variables, sample size.

With kind regards

Karabiner

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There are no simple solutions for that unfortunately. I don't even pay attention to R square. I don't think it receives emphasis in the literature generally.

The real issue is dealing with the time dependency. R square just reflects it.

Yes, but any real data involving humans is never R² = 0.996134, even correlating with time.

That is why time series regression is used.

Only seeing the data will tell.