[s5e20] Bad Night Direct

Trapped by Bonaparte and Black Claw, Adalind chooses the safety of her children over her loyalty to Nick, a move that highlights the impossible position she is in [14]. 💬 Community Consensus

Captain Renard fully commits to his role as a Black Claw puppet, appearing on stage with Adalind and Kelly to present a "perfect family" image for his mayoral campaign [14, 16]. [S5E20] Bad Night

Many viewers expressed frustration with Renard's character arc this season, feeling his "betrayal" was a regression from his previously independent and calculated nature [6]. Trapped by Bonaparte and Black Claw, Adalind chooses

Fans often joke about the "tantrums" in this episode—specifically Nick and Wu’s physical outbursts—as symbols of the extreme pressure the characters are under [21]. Fans often joke about the "tantrums" in this

Fans and critics often highlight "Bad Night" for its atmosphere of impending doom.

The "Stage Scene" with Renard and Adalind is frequently cited as one of the most chilling visual moments of the season, representing Nick’s ultimate nightmare [14]. 📺 Episode Details Original Air Date: May 13, 2016 Director: Norberto Barba Writer: Maya Goldsmith Preceded By: "The Taming of the Wu" Followed By: "Beginning of the End" (Part 1 & 2)

Nick is in a state of pure, unadulterated fury. Having lost Adalind and Kelly to Black Claw, his behavior becomes increasingly erratic and violent, worrying his friends [14, 21].

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: