The Fortune Teller

Fergus Maloney is a fun-loving NYC street vendor from County Cork, Ireland. He hasn’t got a green card per se. But he does have a portable bread oven and one useful gimmick: he tells fortunes with every bread roll he sells. Especially when the customers are stunningly beautiful, like corporate attorney Maria Da Silva.

But when his fortune telling appears to collide with the events of September 11th, things take a darker turn. Caught up in the whirlwind that follows the attacks, Fergus is detained for interrogation, becoming the 51st man in the infamous Camp X-ray. There he will be forced to confront his own dark past.

His interrogator, ambitious young Air Force officer Brandon Zeiss, is plunged into a cat and mouse game with the Irishman; one that will call into question the very values Brandon is sworn to defend. Maria is the only person who can save Fergus, but to do so, she must risk everything she spent her life to build up.

When the whole world comes crashing down, the only thing left is love.

The Fortune Teller is due for release on August 7, 2021

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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: