Chi-kwadraattoets: goodness-of-fit
Hypothesetoetsen in Python
James Chapman
Curriculum Manager, DataCamp
Paarse links
Hoe voel je je als je ontdekt dat je de topbron al hebt bezocht?
purple_link_counts = stack_overflow['purple_link'].value_counts()
purple_link_counts = purple_link_counts.rename_axis('purple_link')\
.reset_index(name='n')\
.sort_values('purple_link')
purple_link n
2 Amused 368
3 Annoyed 263
0 Hello, old friend 1225
1 Indifferent 405
Hypotheses formuleren
hypothesized = pd.DataFrame({
'purple_link': ['Amused', 'Annoyed', 'Hello, old friend', 'Indifferent'],
'prop': [1/6, 1/6, 1/2, 1/6]})
purple_link prop
0 Amused 0.166667
1 Annoyed 0.166667
2 Hello, old friend 0.500000
3 Indifferent 0.166667
$H_{0}$: De steekproef volgt de veronderstelde verdeling
$H_{A}$: De steekproef wijkt af van de veronderstelde verdeling
$\chi^{2}$ meet hoe ver geobserveerde waarden per groep afwijken van de verwachting
alpha = 0.01
Veronderstelde aantallen per categorie
n_total = len(stack_overflow)
hypothesized["n"] = hypothesized["prop"] * n_total
purple_link prop n
0 Amused 0.166667 376.833333
1 Annoyed 0.166667 376.833333
2 Hello, old friend 0.500000 1130.500000
3 Indifferent 0.166667 376.833333
Aantallen visualiseren
import matplotlib.pyplot as plt plt.bar(purple_link_counts['purple_link'], purple_link_counts['n'], color='red', label='Observed')plt.bar(hypothesized['purple_link'], hypothesized['n'], alpha=0.5, color='blue', label='Hypothesized') plt.legend() plt.show()
Aantallen visualiseren
Chi-kwadraat goodness-of-fit-toets
print(hypothesized)
purple_link prop n
0 Amused 0.166667 376.833333
1 Annoyed 0.166667 376.833333
2 Hello, old friend 0.500000 1130.500000
3 Indifferent 0.166667 376.833333
from scipy.stats import chisquare
chisquare(f_obs=purple_link_counts['n'], f_exp=hypothesized['n'])
Power_divergenceResult(statistic=44.59840778416629, pvalue=1.1261810719413759e-09)
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Hypothesetoetsen in Python
