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Python examples and code to replicate the R analysis used in the Computational Technique sections of each chapter from Statistical Modeling: A Fresh Approach (2nd Edition)
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tutorial_10_statistical_modeling_total_and_partial_relationships.ipynb
tutorial_11_statistical_modeling_modeling_randomness.ipynb
tutorial_12_statistical_modeling_confidence_in_models.ipynb
tutorial_14_statistical_modeling_hypothesis_testing_on_whole_models.ipynb
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tutorial_1_statistical_modeling_introduction.ipynb
tutorial_2_statistical_modeling_data.ipynb
tutorial_3_statistical_modeling_describing_variation.ipynb
tutorial_4_statistical_modeling_group-wise_models.ipynb
tutorial_5_statistical_modeling_confidence_intervals.ipynb
tutorial_7_statistical_modeling_mode_formulas_and_coefficients.ipynb
tutorial_8_statistical_modeling_fitting_models_to_data.ipynb
tutorial_9_statistical_modeling_correlation_and_partitioning_of_variation.ipynb

README.md

Statistical Modeling: A Fresh Approach using Python

Python examples and code to replicate the R analysis used in the Computational Technique sections of each chapter from Statistical Modeling: A Fresh Approach (2nd Edition)

A Fresh Approach for Python

The goal of this series of tutorials is to show how all of the R analysis and commands used in the 'Computational Technique' sections of each chapter from 'Statistical Modeling: A Fresh Approach (2nd Edition)' can be done just as easily using the Python programming language. This has the dual goal of introducing 'Scientific Python' to students learning statistics, as well as showcasing the recent advances in statistical computing that have been introduced to Python in recent years. Each tutorial in the series will cover the Computational Technique section of a different chapter from the book, starting with Section 1.4.3 from the introduction.

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