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Practical reporting guidelines, summary of key concepts, test selection parameters table, multiple comparison corrections table.
Control false positives across multiple hypothesis tests with FWER, FDR, Bonferroni, Holm, and Benjamini-Hochberg procedures.
Cohen's d measures effect size separately from statistical significance. Covers practical significance and why small p-values can describe small effects.
Choose sample size and minimum detectable effect with power analysis. Covers underpowered studies, statistical sensitivity, and design tradeoffs.
Understanding false positives, false negatives, statistical power, and the tradeoff between error types. Balance Type I and Type II errors in study design.
One-way ANOVA, post-hoc tests, assumptions, and when to use ANOVA. Compare means across three or more groups while controlling Type I error rates.
F-distribution, F-test for comparing variances, F-test in regression, and nested model comparison.
Covers t-tests including one-sample, two-sample (pooled and Welch), paired tests, assumptions, and decision framework.
Mathematical equivalence between confidence intervals and hypothesis tests, test assumptions (independence, normality, equal variances).
Covers z-tests including one-sample, two-sample, and proportion tests. Topics include when to use z-tests, how to calculate test statistics.
Foundation of hypothesis testing covering p-values, null and alternative hypotheses, one-sided vs two-sided tests.
Covers DBSCAN (Density-Based Spatial Clustering of Applications with Noise), the algorithm.
Covers quadratic programming (QP) for portfolio optimization, including the mean-variance framework, efficient frontier construction.
Covers Vehicle Routing Problem with Time Windows (VRPTW), including mathematical formulation, constraint programming.
Covers minimum cost flow optimization for slotting problems, including network flow theory, mathematical formulation.
Covers Mixed Integer Linear Programming (MILP) for factory optimization, covering mathematical foundations, constraint modeling, branch-and-bound algorithms.
Covers CP-SAT rostering using Google OR-Tools to solve complex workforce scheduling problems with binary decision variables, coverage constraints.
Covers NHITS (Neural Hierarchical Interpolation for Time Series), a deep learning architecture for multi-scale time series forecasting.
Covers N-BEATS, an interpretable deep learning architecture for time series forecasting.
Covers HDBSCAN clustering algorithm covering density-based clustering, automatic cluster selection, noise detection.
Covers hierarchical clustering, including dendrograms, linkage criteria (single, complete, average, Ward), and scikit-learn implementation.
Covers SARIMA (Seasonal AutoRegressive Integrated Moving Average) for forecasting time series with seasonal patterns.
Covers exponential smoothing for time series forecasting, including simple, double (Holt's), and triple (Holt-Winters) methods.
Covers Prophet time series forecasting including additive decomposition, trend modeling, seasonal patterns, and holiday effects.
Covers K-means clustering from mathematical foundations to practical implementation. Topics include the algorithm, initialization strategies.
Covers t-SNE (t-Distributed Stochastic Neighbor Embedding), including mathematical foundations, probability distributions, KL divergence optimization.
Covers LIME (Local Interpretable Model-Agnostic Explanations), including mathematical foundations, implementation strategies.
Covers UMAP dimensionality reduction, including mathematical foundations, fuzzy simplicial sets, manifold learning.
Covers Principal Component Analysis, including mathematical foundations, eigenvalue decomposition.
Covers XGBoost (eXtreme Gradient Boosting), including second-order Taylor expansion, regularization techniques, split gain optimization, ranking loss functions.
Covers SHAP values covering mathematical foundations, feature attribution, and practical implementations for explaining any machine learning model.
Covers LightGBM gradient boosting framework, including leaf-wise tree growth, histogram-based binning, GOSS sampling, exclusive feature bundling.
Covers CatBoost (Categorical Boosting), including categorical feature handling, target statistics, symmetric trees, ordered boosting, regularization techniques.
Covers Isolation Forest covering unsupervised anomaly detection, path length calculations, harmonic numbers, anomaly scoring.
Covers boosted trees and gradient boosting, covering ensemble learning, loss functions, sequential error correction, and scikit-learn implementation.
Covers Random Forest covering ensemble learning, bootstrap sampling, random feature selection, bias-variance tradeoff, and implementation in scikit-learn.
Covers CART (Classification and Regression Trees), including mathematical foundations, Gini impurity, variance reduction.
Covers logistic regression covering mathematical foundations, the logistic function, optimization algorithms, and practical implementation.
Covers Poisson regression for count data analysis. Topics include mathematical foundations, maximum likelihood estimation, rate ratio interpretation.
Spline regression models nonlinear relationships with piecewise polynomials. Covers B-splines, knot selection, natural cubic splines, and Python.
Covers multinomial logistic regression covering mathematical foundations, softmax function, coefficient estimation.
Covers Elastic Net regularization, including mathematical foundations, geometric interpretation.
Model nonlinear relationships with polynomial regression. Covers feature construction, Python implementation, model selection, bias, variance, and overfitting.
Covers Ridge regression and L2 regularization, including mathematical foundations, geometric interpretation, bias-variance tradeoff.
Compare covariance, correlation, and regression for relationships between variables. Covers interpretation, assumptions, pitfalls, and Python examples.
Assess data quality through measurement error, systematic bias, missing-data mechanisms, and outlier detection. Covers diagnosis, treatment, and validation.
Covers statistical modeling fundamentals, including measuring model fit with R-squared and RMSE.
Covers core data visualization techniques including histograms, box plots, and scatter plots. Topics include distributions, identify outliers.
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Why
I created this space so readers can learn together, ask questions, and make sense of difficult ideas.