<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
time = np.array([15,11,7,11,8,9,16,9,15,10,14,9,15,15,10,14])
prom = np.mean(time)
prom
ax = sns.distplot(time,kde=False,bins=30)
prom_tor = (prom)/len(time)
prom_tor
tp = 60/prom
tp
grana = np.array([9,9,10,9,9,9])
prom_grana = np.mean(grana)
prom_grana
tp_g = 60/prom_grana
tp_g
monos = np.array([4.0,10.9,5.3,4.7,7.0,4.1,3.3,4.0,4.1,3.3,4.2,3.3])
prom_monos = np.mean(monos)
prom_monos
tp_m = 60/prom_monos
tp_m
escrito = np.array([6.0,7.8,8.2,12.1,8.0,8.3,7.7,6.9,8.8,6.9,7.9])
prom_escrito = np.mean(escrito)
prom_escrito
tp_e = 60/prom_escrito
tp_e
fruta = np.array([2,2,3,2,2])
prom_fruta = np.mean(fruta)
prom_fruta
tp_f = 60/prom_fruta
tp_f
muestra_uno_hpj = 11*60/(6*60+30)
muestra_uno_hpj
muestra_dos_hpj = 9*60/(8*60+30)
muestra_dos_hpj
hpj_sim = np.random.uniform(round(muestra_dos_hpj,1),round(muestra_uno_hpj,1)+0.1,1000)
prom_hpj_sim = np.mean(hpj_sim)
prom_hpj_sim
pl_mpj_h = 6*60
pl_mpj_l = 5*60+45
mpj_sim = np.random.uniform(pl_mpj_l,pl_mpj_h+1,1000)
prom_mpj = np.mean(mpj_sim)/60
prom_mpj
mpj_p = 21/prom_mpj
mpj_p<jupyter_output><empty_output>
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