Determining Histogram Bin Width using the Freedman-Diaconis Rule
Using the Freedman-Diaconis Rule to determine optimal histogram bin width
more ...Using the Freedman-Diaconis Rule to determine optimal histogram bin width
more ...Assessing goodness of fit using scipy and matplotlib
more ...Updating sample mean and variance to account for new observations without full recalculation
more ...Visualizing the fit of parametric distributions to empirical loss data using ggplot2
more ...A walkthrough of the k-means clustering algorithm
more ...Examination of the LOESS method with implementation in Python
more ...Working with data.table’s special symbols
more ...Derivation of the Poisson as a limiting case of the Binomial PDF
more ...Exploring denisty estimation with various kernels in Python
more ...Assessing the quality of machine learning classifiers with scikit-learn
more ...Python implementation of Newton’s method of polynomial interpolation
more ...A smoothed empirical percentile implementation in Python
more ...Assessing the quality of fit of a Logistic Regression model
more ...Estimating Logistic Regression coefficents in Python
more ...Manual estimation of logistic regression coefficents in R
more ...Derivation of the Normal Equations via Least Squares and Maximum Likelihood
more ...