<jupyter_start><jupyter_text>BOOTSTREPyi= b0+ b1*xi +er<jupyter_code>import numpy as np 
import random 
import matplotlib.pyplot as plt
from math import log

x = np.random.normal(0,1,100)

media = np.median(x)
variancia = np.var(x)
desviop = np.std(x)
print(media)
print(variancia)
print(desviop)
med = []

for i in range(1000):
inx = np.random(1,99)
vet =x(inx)
med= np.median(vet)

print(med)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14150943396226415, 'fraction_numerical': 0.0330188679245283, 'mean_word_length': 5.967213114754099, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '19321', 'license': 'MIT', 'n_tokens_mistral': 197, 'n_tokens_neox': 188, 'n_words': 40, 'path': 'PCA/bootstrep.ipynb', 'repo_name': 'mpmariapaula/web-scraping-jupyter', 'size': '424'}