How It Works
Overview
- How It Works
- The Engine
Deep Dive
Get Started

Free to Start

Connect your agent in 5 minutes
Sign Up

Research

Technical Whitepapers

In-depth statistical methods
Browse Papers
Resources
Learn
Docs
Get Started

Analysis Catalog

Browse 28+ analytics modules
View Catalog

Latest

From the Blog

Insights & stories from real data
Read Blog
Demo
Pricing

Technical Articles

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

Loading articles...

Meet our authors

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

Regularization Techniques: Ridge vs Lasso in Practice - Deep dive into L1 and L2 regularization methods, when to use each approach, and how to implement them effectively through AI assistants for feature selection and model optimization.
Customer Segmentation with K-Means Clustering - Learn how to use K-Means clustering for customer segmentation. Identify distinct customer groups and personalize marketing strategies.

Statistics

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

Technical Articles

No articles found

Stay Updated