# T Test or Pearson's Coefficient for Testing Colinearity of Two Independent Variables

#### Boruchf

##### New Member
I originally found that 23 countries that adopt internationally have a significantly higher rate of COVID19 cases/million than the general popululation of countries.
two tailed p value = .0010

I now want to know if other independent variables can account for these results.

I discovered that all 25 countries that adopt internationally are in the top 35 countries for GDP/capita. People who have more money have money to spend on international adoption.

A t test comparing COVID19 cases/million in the 25 international adopter countries to the COVID19 cases/million in the top 35 GNP/capita countries was not significant, which suggested these two groups are from the same population
Then I looked at Pearson's coefficient.

I compared GDP/capita versus cases COVID19 cases/million for the top 40 GNP/capita countries. Result: The value of R is: 0.2386 The P-Value is .149126.

When I looked at the top 87 GDP/capita countries for GNP versus cases/million there was slight significance:
The value of R is: 0.2496.The P-Value is .022869.

Next I calculated Pearson's coefficient for # adoptions versus COVID19 cases/million in the 25 internationsl adopting countries R is: 0.5752. The P-Value is .002629.

For the purpose of submitting this finding to a journal, for example, does this prove the two explanatory variables are independent, partially independent, or not at all independent?

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