My Review of Jo Nesbo’s ‘Nemesis’

By Graham

Nemesis (Harry Hole, #4)Nemesis by Jo Nesbø
My rating: 2 of 5 stars

In the parcours of every reading adult, books will be encountered that challenge his perception on a deeper level. Books that connect the loose, live wires of his mind and satisfy an aching in his heart. These are the rare books that manage to do what mere human interaction cannot: They transcend the vacuum divide of isolation that separates all of us – teacher from student, husband from wife, brother from brother. Through such books, the writer creates a deep communion with the reader.

Nemesis, by Jo Nesbo, is not one of these books.

It is a book you read to avoid connections, not to make them. It is a book you read when work is hard, and you want something other than a Tuesday evening glass of wine to clean your brain of the meetings and spreadsheets which pay for the rent and the wine alike. It is a book you read on the train, because the alternative is looking out the rain speckled window, and in the four and a half years you have commuted through South Morley and Wenton Village, the scenery has not changed by even one semi-detached house.

Nothing about the protagonist, Harry Hole, seems real to me. I don’t believe the story in the Nemesis could ever remotely happen.

Nor am I meant to. Scandi-crime novels are comforting precisely because they not only have nothing to do with our lives, they also have nothing to do with the lives of actual Nordic police officers.

They are comforting because I know every tired cliche that will befall Hole before I break the paperback’s spine. I know it before his new partner – a young female cadet with the best marks in the policy academy – makes her first appearance on page 50. I know it before his relationship fails; before he breaks down and pours his first whiskey; before he arrests the obvious yet ultimately innocent suspect, then is forced to release him under severe reprimand from the police chief, twenty pages later.

And that’s just as we, the readers of Scandi-crime novels, want it. Because spreadsheets and management meetings are a pain in the ass. And so are commuter trains.

For this, we show our thanks in the only way Jo Nesbo – artist that he is – truly appreciates:

We buy the next one.

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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: