<jupyter_start><jupyter_text><jupyter_code># import libraries
import math
import scipy.stats as st
#69.5, 67.3, 73.5, 71.3, 70, 69.4, 69.5, 66.2, 71.4, 73.4, 63.3, 68.8, 69.9, 71.3, 72.5, 62.3, 63.5, 66.6, 64.5, 66.6, 60.9, 63.5, 66.4, 73.4, 77.1, 75.3, 70.1, 69.4, 65.7, 68.5, 
# quick stats
data = [69.9, 70.8, 70.2, 69.8, 68.8, 66.4, 65.5, 64.4, 63.6, 63.3]
n = len(data)
mean = sum(data)/n
s_variance = (1/(n - 1)) * (sum([k ** 2 for k in data]) - (n * (mean ** 2)))
s_stdev = math.sqrt(s_variance)

print(n)
print('x_bar:',round(mean, 6),'sample_sd:', round(s_stdev, 6), 'sample_variance:', round(s_variance, 6))
# z-value => get area from z-value
z = 2.68
area = round(st.norm.cdf(z), 4);
print("area:",area, "1-area:", round(1-area,4))
# z-value => get z-value from area
area = 0.05
z = round(st.norm.ppf(area)*-1, 4);
print("z:",z)
# t-value => get area from t-value
alpha = 0.325
df = 1
area = round(st.t.cdf(alpha, df), 4);
print("area:",area, "1-area:", round(1-area,4))
# t-value => get t-value from area
area = 0.025
df = 9
alpha = round(st.t.ppf(area, df) * -1, 4);
print("alpha:",alpha)<jupyter_output>alpha: 2.2622
{'fraction_non_alphanumeric': 0.2415929203539823, 'fraction_numerical': 0.1407079646017699, 'mean_word_length': 3.7322175732217575, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 8, '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': '84393', 'license': 'MIT', 'n_tokens_mistral': 648, 'n_tokens_neox': 542, 'n_words': 134, 'path': 'MiscStats.ipynb', 'repo_name': 'wasurocks/PowerStats', 'size': '1130'}