The Pleasure of Finding Things Out
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The Pleasure of Finding Things Out

Determining Histogram Bin Width using the Freedman-Diaconis Rule

Using the Freedman-Diaconis Rule to determine optimal histogram bin width

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Assessing Goodness-of-Fit Quantitatively and Visually

Assessing goodness of fit using scipy and matplotlib

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Online Mean and Variance Update without Full Recalculation

Updating sample mean and variance to account for new observations without full recalculation

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The Beta Function and its Variants

An Investigation of the Complete and Incomplete Beta Functions with use cases in Statistical Modeling

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LOESS - Nonparametric Scatterplot Smoothing in Python

Examination of the LOESS method with implementation in Python

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The Empirical Bootstrap for Confidence Intervals in Python

Computing non-parametric bootstrap confidence intervals with Python

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Sampling from Mixed Exponential Distributions using the Inverse Transform Method

A method for obtaining mixed exponential distirbution quantiles for statistical analysis

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Derivation of the Poisson Distribution as a Limiting Case of the Binomial PDF

Derivation of the Poisson as a limiting case of the Binomial PDF

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Kernel Density Estimation in Python

Exploring denisty estimation with various kernels in Python

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Polynomial Interpolation: Newton’s Method

Python implementation of Newton’s method of polynomial interpolation

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Smoothed Empirical Percentile for Percentile Matching

A smoothed empirical percentile implementation in Python

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Bessel’s Correction

Correcting the bias in estimators for the population variance.

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Estimating Regression Coefficients with Batch Gradient Descent

A walk-thru and demonstration of the batch gradient descent algorithm in Python

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Goodness of Fit and Significance Testing for Logistic Regression Models

Assessing the quality of fit of a Logistic Regression model

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Estimating Logistic Regression Coefficents From Scratch (Python version)

Estimating Logistic Regression coefficents in Python

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Estimating Logistic Regression Coefficents From Scratch (R version)

Manual estimation of logistic regression coefficents in R

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Derivation of the Normal Equations

Derivation of the Normal Equations via Least Squares and Maximum Likelihood

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  • Links

    • The Python Standard Library
    • scikit-learn
    • Scipy.org
    • Journal of Machine Learning Research
    • Journal of Statistical Software
    • The Journal of Open Source Software
    • Numerical Algorithms Group
    • Free Software Foundation
    • Casualty Actuarial Society
    • Continuum Analytics
    • American Statistical Association
    • SIAM
    • American Mathematical Society
    • IEEE
    • FermiLab
    • Beyond Your Plateau

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