Causation in Populations: Exercise 2-2

Exercise Context:
Let's consider another effect and some of the causal factors that might be related to it. The effect is a car battery losing its charge. The potential causal factors are: a) leaving the headlights on for over two hours (or not), b) the age of the battery being over two years (or not), and c) there being an electrical short in the internal plates of the battery (or not).

Let's define the causal structure in this case by using a table:
Situation Causal Factor 1 Causal Factor 2 Causal Factor 3 Effect
1 Lights on 2 hours Over 2 years old Short in plates Loses charge
2 Lights on 2 hours Over 2 years old No short Loses charge
3 Lights on 2 hours Not over 2 years old Short in plates No charge lost
4 Lights on 2 hours Not over 2 years old No short No charge lost
5 Lights not on Over 2 years old Short in plates Loses charge
6 Lights not on Over 2 years old No short No charge lost
7 Lights not on Not over 2 years old Short in plates No charge lost
8 Lights not on Not over 2 years old No short No charge lost
Here is another table that dislays the percentages of car batteries in a population in each of four of of the possible causal situations:
Situation Percentage in the Situation
2 5%
4 10%
5 5%
8 80%
This kind of account of the percentages of a population that have some properties (in this case a causal situation) is often known as a "distribution" of the properties. If we assume that all batteries in all cars are governed by the partial causal structure given in table X and that causal structure has all the relevant causal factors, then in the population described above, 10 % lose their charge.

This is an applet that allows you to vary the distribution of causal situations in the population of car batteries.
Question:
Create a population of batteries that displays the following features:
01 All eight causal situations are represented in the population
02 15% of the batteries will lose their charge
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Proceed to Exercise:
1 2-1 2-2