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Technical Articles

Comprehensive guides on statistical analysis, machine learning, and AI-powered data workflows

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Technical Articles by Category

Hypothesis Testing

ANOVA: Analysis of Variance - ANOVA for comparing group means: assumptions, one‑way vs two‑way, effect sizes, post‑hoc tests, and robust alternatives.
Chi‑Square Testing: Practical Guide - Chi‑Square testing for independence and goodness‑of‑fit. Assumptions, effect sizes, and interpretation.
t‑Test: Independent, Paired, Welch - Guide to t‑tests: independent vs paired, Welch for unequal variances, effect sizes (Cohen’s d/Hedges’ g), assumptions, and alternatives.
A/B Testing at Scale: Statistical Significance Made Simple - From t-tests to advanced experimental design, discover how to run statistically sound A/B tests using AI assistants to interpret results and make data-driven decisions.

Merchandising

Association Rules (Apriori): Practical Guide - Mine association rules with Apriori/FP‑Growth. Understand support, confidence, lift, conviction, constraints, and validation.

Experimentation

Bayesian A/B Testing: Practical Guide - Bayesian A/B testing with posterior probabilities, credible intervals, expected loss, ROPE, and sequential monitoring.

Retention & Value

BG/NBD + Gamma‑Gamma for LTV - Use BG/NBD for purchase frequency and Gamma‑Gamma for spend to forecast customer lifetime value with calibration and cohorts.

Causal Inference

Causal Impact: Practical Guide - Causal Impact with Bayesian structural time series: pre-period modeling, counterfactual forecast, credible intervals, and interpretation.
Difference-in-Differences: Practical Guide - Difference-in-Differences (DID): parallel trends, event studies, staggered adoption estimators, and interpretation with diagnostics.
Propensity Score Matching: Practical Guide - Learn how Propensity Score Matching estimates causal effects from observational data. Assumptions, matching strategies, balance diagnostics, and interpretation.
Synthetic Control: Practical Guide - Synthetic Control: donor pool selection, pre-period fit, placebo tests, and interpretation for one-off interventions.

Regression

Elastic Net Regression: Practical Guide - Elastic Net blends L1/L2 penalties to stabilize coefficients under collinearity while enabling feature selection. Includes tuning and diagnostics.
Linear Regression: Practical Guide - OLS linear regression: interpretation, assumptions, diagnostics, multicollinearity, alternatives, and reporting best practices.
Ridge Regression: Practical Guide - Ridge (L2) regression: when to use it, how to tune alpha, preprocessing steps, diagnostics, and how it compares to Lasso/Elastic Net.

Clustering

K‑Means Clustering: Practical Guide - K‑Means clustering: standardization, choosing K, stability, profiles, and interpretation.

Classification

Logistic Classification: Practical Guide - Logistic regression for binary classification: odds ratios, regularization, metrics, calibration, and interpretation.

Dimensionality Reduction

PCA: Principal Component Analysis Guide - PCA for dimensionality reduction: standardization, variance explained, loadings, biplots, selection of components, and caveats.

Economics

Price Elasticity: Practical Guide - Estimate price elasticity, account for promotions/seasonality, and use elasticity to simulate revenue‑optimal prices.

Forecasting

Prophet Forecasting: Practical Guide - Prophet: seasonality, holidays, changepoints, and interpretation. How to prepare data and validate forecasts.

Ensemble

Random Forest: Practical Guide - Random Forest: bagging, OOB, tuning, importances, and best practices.

Customer Analytics

RFM Segmentation: Practical Guide - Compute RFM scores, map to intuitive segments, and use profiles to target campaigns, retention, and win‑back.

Survival

Cox Proportional Hazards: Practical Guide - Cox Proportional Hazards: time‑to‑event modeling with censoring, hazard ratios, proportionality checks, and interpretation.

Time Series

VAR: Multivariate Time Series Guide - Vector Autoregression (VAR): data prep, stationarity, lag order, stability, Granger causality, IRFs, FEVD, and when to use VECM.

Boosting

XGBoost: Practical Guide - XGBoost on tabular data: tuning, metrics, SHAP explanations, and common pitfalls.

Tutorial

Linear Regression: From Basics to Advanced - Complete guide to linear regression analysis. Learn assumptions, diagnostics, and interpretation of results with practical examples.

AI Workflows

Building AI-First Data Analysis Pipelines - Learn how to create end-to-end data analysis workflows where AI assistants orchestrate statistical tools, manage data transformations, and generate insights automatically.

Statistical Methods

Advanced Time Series Forecasting with ARIMA and MCP - Master the art of time series analysis using ARIMA models through MCP Analytics, with practical examples from business forecasting and seasonal decomposition.

Exploratory Analysis

Correlation vs Causation: What Your Data Really Tells You - Navigate the complex relationship between correlation and causation in data analysis, with practical guidance on interpreting correlation matrices and avoiding common pitfalls.

MCP Integration

Desktop Extensions: The MCP Revolution for Statistical Analysis - How Claude Desktop Extensions are transforming statistical analysis accessibility through the Model Context Protocol, making professional-grade tools available to everyone.

Machine Learning

Statistics

Survival Analysis with Cox Regression - Apply survival analysis techniques to predict customer churn, equipment failure, and medical outcomes using Cox proportional hazards models.

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