<jupyter_start><jupyter_text>Regression model based on ordinary least squares Ejemplo de conceptos estadísticos<jupyter_code>import statsmodels.api as sm

model = sm.OLS(y, X)
model.fit()
results = model.fit()
results.summary()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10116731517509728, 'fraction_numerical': 0.0, 'mean_word_length': 7.896551724137931, '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': '615136', 'license': 'Apache-2.0', 'n_tokens_mistral': 92, 'n_tokens_neox': 91, 'n_words': 21, 'path': 'week8_ML_lr_knn_encoder/day4_logistic_regresion_confusion_matrix/theory/linear_regression_v2.ipynb', 'repo_name': 'JaunIgciona/DataBootcamp_Nov2020', 'size': '257'}