-integrale De Kaamelott -fr--dvdrip- -

Kadoc entered, chewing noisily. “What? That old thing? I found it near the latrines. It had a note: ‘-FR–DVDRIP-’. Probably means ‘For Rotten—Don’t Retrieve, It’s Pee-soaked.’”

Arthur pinched the bridge of his nose. “That’s not a language. And DVDRIP isn’t a magical artifact. It’s a format for—never mind. Where did you find it?” -integrale de Kaamelott -FR--DVDRIP-

Perceval’s eyes widened. “A shopping list? For what? Dragon eggs? Holy grails?” Kadoc entered, chewing noisily

Perceval gasped. “You defiled the sacred intégrale!” I found it near the latrines

And so ended the quest for the legendary -integrale de Kaamelott -FR--DVDRIP- —a treasure only Perceval would mourn, and only Kadoc would wipe his boots on.

“In Kadoc’s tent! He was using it as a cheese board.”

“Arthur! Great news! I found the Intégrale !” Arthur didn’t look up. “The intégrale of what, Perceval?” “Of the knight training manuals! The legendary DVDRIP! It’s a… a stone tablet that contains all the knowledge of the old kingdom. But it’s in a foreign language. I think it’s ‘FR’ for ‘Faraway Realms’.”

As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:

While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values: