## What is life?

##### By Graham

Life is that time you played

What started out as hide-and-seek

Secret bases, boys-vee-girls

And finally a rope swing and a dare

Water so cold it warmed the after-swimming air.

Treats meant to last the week eaten then and there.

Nothing ever tasted so sweet.

No one saw the June sun duck below the trees

behind the river line.

Well past supper time!

On legs sore from running you nonetheless peeled

Across the fields

To a half-meant ‘sorry momma’, and a half-cold spaghetti meal.

Life is the buzzing sound

From music played too loud

And the noise of the college crowd.

That lingers still in ears and on your clothes

All down September’s rain-painted side-walk home,

Reminds you of that second pint,

The little smile she bore you

Sideways, mid sentence to her friends,

That might – just might – some future night, blossom into much, much more.

Life is those endless minutes waiting, anticipating

The breaking of the swept and polished order of long-kept

Knick-knacks, unmoved since last They stormed the door;

That in two violent minutes of shedding little coats and mittens triggered

More noise than needles make in all the winter weeks of knitting new ones, one size bigger.

Then They finally appear, you find

Little faces so filled with Now and so in likeness of Their parent-child, standing, smiling just behind,

It calls to mind, every school lunch prepared, every memory shared

and all those times bygone.

This rich reward, this weekend chaos, is the reason why you struggle on.

Life is never ‘staying safe’,

Waiting, clutching to existence,

Until every living risk fades to nothing.

Category: Art Poetry

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:

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.

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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:

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:

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