A data science tutorial using a confusion matrix to calculate the real probability of having a disease after a positive diagnostic test result.
A guide to manually generating predicted values for logistic regression using matrix multiplication in R, as an alternative to the predict() function.
A technical guide to performing multilevel multinomial conjoint analysis using R, Bayesian modeling, and statistical packages.
A detailed analysis of an optimal stopping problem involving drawing cards for reward, exploring mathematical strategies and first-principles reasoning.
A technical exploration of using pairwise likelihood in linear mixed models with complex sampling, comparing results from svylme and lme4 packages.
Explores the challenges of applying signed rank tests to complex survey data and proposes a design-independent rank transformation method.
A technical discussion on the 'fourth-root' condition for estimator consistency in statistical models like GEE, exploring asymptotic theory and nuisance parameters.
Explains the core theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Explains statistical sampling using a Go program example to estimate population percentages, highlighting its power and practical limits in tech contexts.
A humorous analysis exploring the correlation between the number of metal bands per capita and national happiness scores in European countries.
A tutorial on visualizing mixed effect regression models and their uncertainty using non-parametric bootstrapping in R with ggplot2.
Explains why pairwise independence of variables does not imply joint independence, using a chessboard as an intuitive counterexample.
Explores convolutions in probability theory, explaining how they combine distributions and compute sums of random variables.
Explores the connection between the Welch-Satterthwaite t-test and linear regression using the sandwich variance estimator.
A guide to calculating marginal and conditional effects in generalized linear mixed models (GLMMs) using the R {marginaleffects} package.
A statistical analysis of estimating a normal distribution using binary (yes/no) predictions from multiple scientists, applied to a temperature forecasting problem.
Article discusses SQLite's limited built-in functions, compares it to other databases, and introduces a Go-based standard library extension.
A guide explaining marginal effects in regression analysis, including definitions and differences between types like average marginal effects, using R packages.
A guide to creating confidence intervals for evaluating machine learning models, covering multiple methods to quantify performance uncertainty.
A technical guide explaining methods for creating confidence intervals to measure uncertainty in machine learning model performance.
