Skip to content

on_boarding.md

Latest commit

 

History

History
111 lines (70 loc) · 8.47 KB

on_boarding.md

File metadata and controls

111 lines (70 loc) · 8.47 KB
graph LR
    Data_Management_Utilities["Data Management & Utilities"]
    Assumption_Testing_Diagnostics["Assumption Testing & Diagnostics"]
    Core_Statistical_Tests["Core Statistical Tests"]
    Regression_Modeling["Regression & Modeling"]
    Specialized_Statistical_Methods["Specialized Statistical Methods"]
    Post_Analysis_Augmentation["Post-Analysis & Augmentation"]
    Visualization_Plotting["Visualization & Plotting"]
    Data_Management_Utilities -- "provides data to" --> Assumption_Testing_Diagnostics
    Data_Management_Utilities -- "provides data to" --> Core_Statistical_Tests
    Data_Management_Utilities -- "provides data to" --> Regression_Modeling
    Data_Management_Utilities -- "provides data to" --> Specialized_Statistical_Methods
    Data_Management_Utilities -- "provides data to" --> Visualization_Plotting
    Assumption_Testing_Diagnostics -- "informs" --> Core_Statistical_Tests
    Assumption_Testing_Diagnostics -- "provides diagnostic results to" --> Visualization_Plotting
    Core_Statistical_Tests -- "provides raw results to" --> Post_Analysis_Augmentation
    Core_Statistical_Tests -- "provides raw results to" --> Visualization_Plotting
    Regression_Modeling -- "provides model results to" --> Post_Analysis_Augmentation
    Regression_Modeling -- "provides model results to" --> Visualization_Plotting
    Specialized_Statistical_Methods -- "provides specialized results to" --> Post_Analysis_Augmentation
    Specialized_Statistical_Methods -- "provides specialized results to" --> Visualization_Plotting
    Post_Analysis_Augmentation -- "provides augmented results to" --> Visualization_Plotting
    click Data_Management_Utilities href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pingouin/Data_Management_Utilities.md" "Details"
    click Core_Statistical_Tests href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pingouin/Core_Statistical_Tests.md" "Details"
    click Regression_Modeling href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pingouin/Regression_Modeling.md" "Details"
    click Visualization_Plotting href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/pingouin/Visualization_Plotting.md" "Details"
Loading

CodeBoardingDemoContact

Details

The pingouin library is structured around a clear data flow, starting with Data Management & Utilities which prepares and validates data, primarily as Pandas DataFrames. This processed data then flows into Assumption Testing & Diagnostics for validating statistical assumptions, and directly into Core Statistical Tests, Regression & Modeling, and Specialized Statistical Methods for various analytical computations. The results from these core analytical components are then passed to Post-Analysis & Augmentation for further processing like effect size calculation, multiple comparison corrections, and power analysis. Finally, all analytical outputs can be channeled to Visualization & Plotting for graphical representation, completing the analytical pipeline. This modular design ensures a logical progression from data preparation to insightful visualization, with clear boundaries for each functional area.

Data Management & Utilities [Expand]

This foundational component is responsible for all data-related operations, including loading datasets, preprocessing, validation, and providing general utility functions. It ensures that data is in the correct and consistent format (primarily Pandas DataFrames) for subsequent statistical analyses.

Related Classes/Methods:

  • utils

Assumption Testing & Diagnostics

This component provides a suite of functions to test for statistical assumptions (e.g., normality, sphericity) and perform other diagnostic checks. These tests are crucial for validating the applicability of various statistical methods and informing the choice between parametric and non-parametric approaches.

Related Classes/Methods:

Core Statistical Tests [Expand]

This is the central component for hypothesis testing, encompassing a wide array of fundamental statistical tests. It includes implementations for parametric tests (ANOVA, t-tests), non-parametric alternatives, various correlation analyses, contingency table analyses, and pairwise comparisons.

Related Classes/Methods:

Regression & Modeling [Expand]

Dedicated to statistical modeling, this component provides functionalities for linear and logistic regression, as well as more specialized analyses such as mediation. It handles the fitting and analysis of predictive models.

Related Classes/Methods:

Specialized Statistical Methods

This component houses functions for niche statistical analyses that require unique methodologies, such as circular statistics for directional data. It caters to specific analytical needs not covered by the broader core tests.

Related Classes/Methods:

Post-Analysis & Augmentation

This component enhances the interpretation and robustness of statistical findings. It includes tools for calculating effect sizes, applying multiple comparison corrections to control error rates, and performing power analyses to determine optimal sample sizes or statistical power.

Related Classes/Methods:

Visualization & Plotting [Expand]

This component provides utilities for generating common statistical plots. It enables users to visualize data distributions, relationships between variables, and the results of statistical tests, facilitating data exploration and communication of findings.

Related Classes/Methods:

FAQ