# Computer = brute force

If you have clicked the button a few times, you've seen that the results vary quite a bit. Here are the first ten results that I got:

Trial #: |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |

Mondays/Fridays | 43 | 33 | 37 | 47 | 40 | 37 | 39 | 41 | 41 | 51 |

What have we accomplished so far? First, we made a hypothesis about what causes sickdays -- namely, they happen on randomly chosen days. The competing hypothesis, held by the pointy-haired boss, is that sickdays occur disproportionately on Mondays and Fridays.

First step: pick a hypothesis | |

Secondly, we calculated how many sickdays "should" fall on Mondays or Fridays according to
our hypothesis -- that is, 40%, or 40 out of 100. This is the **expected** value of
sickdays. But since we're dealing with a random process, we also expect some scatter around that
expected value. The key question is, how much scatter?

Second step: calculate the expected result | |

Next we used a simulation of randomly chosen weekdays to investigate how much scatter to expect around the 40 out of 100 prediction.

Third step: simulate the scatter | |

### In the 10 trials, listed above, how common was it to get 42 or more Monday/Friday sickdays?

(To make this problem interactive, turn on javascript!)

- I need a hint ... : Try counting how many trials had at least 42 Mon/Fri sickdays

- ...another hint ... : 3 out of 10 trials had at least 42 Mon/Fri sickdays -- what percentage is that?

#### I think I have the answer: 3 out of 10 trials had at least 42 Mon/Fri sickdays,

or 30%, which seems pretty common.

Time for a donut break!

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