These consequences of a corrupt political system can adversely effect
productivity, growth, and GDP per capita.
In sum, more corruption leads to lower GDP per capita.
Or, it might be something else more clever.

b)
(20) Whichever way you’ve chosen to model this problem, execute a linear regression, calculating the slope and intercept
estimators in EXCEL (or Google Spreadsheet) using the “by hand” method that was demonstrated in the lectures.
Report
your Betahats. [
SEE BELOW.
YOUR BETAS AND OTHER KEY RESULTS SHOULD BE SEPARATED FROM THE
DATA SO THEY’RE EASY TO SEE.
I’VE CALCULATED SOME VALUES HERE THAT WERE NOT ACTUALLY
ASKED FOR IN THE HW, IN PARTICULAR, THE INGREDIENTS FOR R-SQUARED AND THE VARIANCES OF
THE BETAHATS.]

c)
(10) Suppose the corruption measure was flipped.
That is, the least corruption possible is rated a 10, and the highest
corruption level is zero.
Argentina, for instance, was originally 8, is now
2.
Australia was originally 1.3, and is now 8.7.
Describe how this will change the slope, and intercept of your regression from above.
If you can do this without recalculating
the whole regression, that would be prefered. [To start, write out a simple formula to describe this transformation.
Then apply
the transformed variable to the slope/intercept formulas for the regression.]

AT LEAST SOME OF THE MATH.
BUT GIVE 8 POINTS FOR MORE “INTUITIVE” EXPLANATIONS ON THIS
PROBLEM.]
a)
If independent, then C is the “X” variable in the regression.