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Learning Medical Statistics with Python and Jupyter Notebooks
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- 1 Chapter01_Audience_Aims_Motivation_vignette
- 2 Audience and aims
- 3 Motivation
- 4 Chapter02_Sneak_peek_vignette
- 5 A sneak peek
- 6 Chapter03_Why_python_vignette
- 7 Why use python
- 8 Python
- 9 Update_Jupyter_notebooks
- 10 Chapter04_Jupyter_notebook_vignette
- 11 The Jupyter Notebook
- 12 Installing the seaborn module
- 13 Chapter05_A_closer_look_at_our_data_vignette
- 14 A closer look at our dataset
- 15 Chapter06_Spreadsheet_software_vignette
- 16 Spreadsheet software
- 17 Chapter07_Plotly_vignette
- 18 Introduction to plotly
- 19 Chapter08_Research_types_vignette
- 20 Research_types
- 21 Chapter09_Data_types_vignette
- 22 Data types
- 23 Chapter10_Pandas_vignette
- 24 Introduction to Pandas part 1
- 25 Introduction to Pandas part 2
- 26 Chapter11_Measures_of_Central_tendency_and_dispersion_vignette
- 27 Measures of central tendency and measures of dispersion
- 28 Chapter12_Relating_probability_and_area_vignette
- 29 The connection between probability and area
- 30 Chapter13_The_central_limit_theorem_vignette
- 31 The central limit theorem
- 32 Chapter14_Z_and_t_distributions_vignette
- 33 Z and t distributions part 1
- 34 Z and t distributions part 2
- 35 Chapter15_Hypotheses_vignette
- 36 Hypotheses
- 37 Chapter16_Confidence_intervals_vignette
- 38 Confidence intervals
- 39 Chapter17_Parametric_and_nonparametric_tests_vignette
- 40 Parametric and nonparametric tests
- 41 Chapter18_Comparing_two_means_vignette
- 42 Comparing the means of two groups
- 43 Chapter19_Comparing_categorical_data
- 44 Comparing categorical data
- 45 Chapter20_Linear_regression
- 46 Linear regression
- 47 Chapter21_Sensitivity_specificity_PPV_NPV_vignette
- 48 Sensitivity Specificity PPV and NPV
