PyStatsV1 documentation ======================= Welcome to the documentation for **PyStatsV1** – chapter-based applied statistics examples in plain Python, mirroring classical R textbook analyses. Students (recommended): install from PyPI and start the Workbook ---------------------------------------------------------------- If you're a student (not a developer), the easiest path is to install the Workbook bundle from PyPI and let the CLI create a local copy of the labs for you. .. code-block:: bash python -m pip install "pystatsv1[workbook]" pystatsv1 workbook init --dest my_workbook Then run the built-in checks as you work: .. code-block:: bash cd my_workbook pystatsv1 workbook check If you're on Windows 11 and this is your first time installing Python, start here: :doc:`workbook/windows11_setup`. Lightweight install (no Workbook checks) ---------------------------------------- If you only want the core helper package (without the Workbook checks bundle), you can install the base package directly from PyPI: .. code-block:: bash python -m pip install pystatsv1 Developers / Contributing ------------------------- If you want the full chapter-based labs (simulators, scripts, Makefile targets, and tests), clone the GitHub repository and install in editable mode: .. code-block:: bash git clone https://github.com/pystatsv1/PyStatsV1.git cd PyStatsV1 python -m pip install -e . See :doc:`getting_started` and :doc:`contributing` for the contributor workflow. .. toctree:: :maxdepth: 1 :caption: Workbook – Student Labs (recommended starting point) workbook/index .. toctree:: :maxdepth: 2 :caption: Track A – Applied Statistics with Python (Regression) getting_started applied_stats_with_python_intro applied_stats_with_python_ch2_r_basics applied_stats_with_python_ch3_data_and_programming applied_stats_with_python_ch4_summarizing_data applied_stats_with_python_ch5_probability_and_statistics applied_stats_with_python_ch6_resources applied_stats_with_python_ch7_simple_linear_regression applied_stats_with_python_ch8_inference_for_simple_linear_regression applied_stats_with_python_ch9_multiple_linear_regression applied_stats_with_python_ch10_model_building applied_stats_with_python_ch11_categorical_predictors_and_interactions applied_stats_with_python_ch12_analysis_of_variance.rst applied_stats_with_python_ch13_model_diagnostics applied_stats_with_python_ch14_transformations applied_stats_with_python_ch15_collinearity applied_stats_with_python_ch16_variable_selection_and_model_building applied_stats_with_python_ch17_logistic_regression applied_stats_with_python_ch18_beyond chapters teaching_guide contributing .. _psych-track: .. toctree:: :maxdepth: 1 :caption: Track B – Psychological Science & Statistics (Psych track) psych_intro psych_ch1_thinking_like_a_scientist psych_ch2_ethics psych_ch3_measuring_variables psych_ch4_distributions psych_ch5_central_variability psych_ch6_normal_zscores psych_ch7_probability_sampling psych_ch8_hypothesis_testing psych_ch9_one_sample_ci psych_ch10_independent_t psych_ch11_paired_t psych_ch12_one_way_anova psych_ch13_two_way_anova psych_ch14_repeated_measures_anova psych_ch14a_pingouin_appendix psych_ch15_correlation psych_ch15a_pingouin_appendix psych_ch16_regression psych_ch16a_pingouin_regression psych_ch16b_pingouin_regression psych_ch17_mixed_models psych_ch18_ancova psych_ch19_nonparametrics psych_ch19a_rank_nonparametrics psych_ch20_responsible_researcher .. _psych-track-c: .. toctree:: :maxdepth: 1 :caption: Track C – Problem Sets & Worked Solutions (Psych track) psych_track_c_overview psych_ch10_problem_set psych_ch11_problem_set psych_ch12_problem_set psych_ch13_factorial_anova psych_ch14_problem_set psych_ch15_problem_set psych_ch16_problem_set psych_ch17_problem_set psych_ch18_problem_set psych_ch19_problem_set psych_ch20_problem_set .. _business-track-d: .. toctree:: :maxdepth: 1 :caption: Track D – Business Statistics & Forecasting for Accountants business_intro business_ch01_accounting_measurement business_ch02_double_entry_and_gl business_ch03_statements_as_summaries business_ch04_assets_inventory_fixed_assets business_ch05_liabilities_payroll_taxes_equity business_ch06_reconciliations_quality_control business_ch07_preparing_accounting_data_for_analysis business_ch08_descriptive_statistics_financial_performance business_appendix_ch08_milestone_big_picture business_ch09_reporting_style_contract business_ch10_probability_risk business_ch11_sampling_estimation_audit_controls business_ch12_hypothesis_testing_decisions business_ch13_correlation_causation_controlled_comparisons business_ch14_regression_driver_analysis business_appendix_ch14_milestone_track_d_data business_appendix_ch14b_nso_v1_data_dictionary business_appendix_ch14c_ch14_artifact_dictionary business_appendix_ch14d_artifact_qa_checklist_big_picture business_appendix_ch14e_apply_to_real_world business_ch15_forecasting_foundations business_ch16_seasonality_baselines business_ch17_revenue_forecasting_segmentation_drivers business_ch18_expense_forecasting_fixed_variable_step_payroll business_ch19_cash_flow_forecasting_direct_method_13_week business_ch20_integrated_forecasting_three_statements business_ch21_scenario_planning_sensitivity_stress business_ch22_financial_statement_analysis_toolkit business_ch23_communicating_results_governance business_capstone_nso_close_clean_explain_forecast_decide business_capstone_templates business_capstone_rubric business_appendix_pdf_refresher business_appendix_authoring_rules