Descriptive Statistics in Python: A Practical Analytical Guide
Introduction In our previous post, we covered the theory behind descriptive statistics — the formulas, notation, and intuition. Here, we
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Python tips for statistical computing, data analysis, and data visualization to assist data modellers, statisticians, data scientists, and researchers.
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Linear Model in Python
For theoretical developments, refer In Python, linear models can be directly implemented using libraries such as statsmodels (via statsmodels.formula.api.ols) or
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The Art of Visualization with Plotly
The CRAFT framework provides a structured and systematic approach to data visualization. This framework is platform-independent — it is not
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Conjugate Bayesian Models Python
This page presents Python code for implementing Bayesian conjugate models using standard prior–likelihood formulations and their associated posterior distributions. For
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GLM Algorithms and Python Libraries
SKlearn and Statsmodels Choosing the Right Link Function Generalised Linear Models (GLM) offer a flexible framework for regression analysis. When