Ordinal and Multinomial Logistic Regression in Excel

BESH Stat NG v0.5.8 adds two important capabilities for applied regression work in Excel. First, it introduces ordinal logistic regression and multinomial logistic regression workflows that can be used directly from worksheet formulas. Second, it adds the new formula= argument to regression UDFs. Now, it is possible to specify richer model directly in the worksheet, …

Survival Analysis in Excel with BESH Stat NG: Kaplan–Meier, Log-Rank, and Cox Regression

This tutorial shows how to analyze time-to-event data (survival analysis) in Excel with BESH Stat NG using a real medical dataset. We compare clinics with Kaplan–Meier curves and the log-rank test, then fit a Cox proportional hazards model with clinic, prison record, and methadone dose as predictors.

BESHStatNG 0.4.6.0 is out: 3D Objects for XYZ Scatterplot, first batch of Excel UDFs, and Auto Update

BESHStatNG v0.4.6.0 delivers improvements aimed at making the add-in more visual, more “worksheet-native”, and easier to keep up to date: XYZ 3D Scatterplot enhancements, including 3D wireframe objects (sphere and ellipsoid) and axis flipping. A first batch of Excel User Defined Functions (UDFs) covering distribution CDFs, selected nonparametric tests, and survival-analysis related tests. A new …

Survival Analysis in Excel with BESHStatNG: Kaplan–Meier, Log-rank, and Cox Regression

If you work with time-to-event data (clinical outcomes, reliability, churn/retention, follow-up studies), you already know the pain: survival analysis is powerful, but many workflows pull you out of Excel and into a separate stats stack. BESHStatNG brings survival analysis directly into Excel—so you can prepare data, run analyses, and generate publication-ready outputs where your team …

BESHStatNG v0.4.0: Publication-ready Chart Export + Animated 3D XYZ Scatterplot GIFs

The latest BESHStatNG release (v0.4.0) adds two highly requested features: (1) a dedicated Chart Export dialog for publication-quality figures, and (2) an Animated GIF workflow for the 3D XYZ scatterplot. Both features work directly inside Excel and do not require installing external tools or scripting environments.

BESHStatNG Update v0.3.2: New Agreement Module for Method Comparison & Reliability

A new version of BESHStatNG is now available, and it introduces a major addition: the new Agreement module. This release is focused on method comparison and reliability workflows commonly used in laboratory medicine, medical research, and quality assessment.

BESHstatNG v0.3.0 released — spotlight on GLM, NB2, and Zero-Inflated Poisson

The first official release of BESHstatNG (v0.3.0) is now available — an open-source Excel add-in (Excel-DNA + VB.NET, installed via MSI) that continues the original VBA-based BESHstat tradition with very similar coverage of statistical tests and methods, now on a modern and maintainable foundation. Get BESHstatNG v0.3.0 Browse all docs

Nonparametric methods in BESHStatNG: exact p-values (even with ties) at Excel speed

If you do statistics in Excel, nonparametric tests are often the difference between “I can analyze this now” and “I need to export to another tool.” BESHStatNG keeps the workflow Excel-first, while providing a compiled engine and robust implementations of the methods people use most. This post is a quick update on what’s already implemented …

BESHStatNG is happening: a modern VB.NET re-implementation of the classic BESHStat Excel add-in

If you’ve used the original BESHStat (VBA / .xlam) add-in over the years, you already know the idea: keep statistics close to the spreadsheet and make analysis fast and practical. Now I’m rebuilding it as BESHStatNG (New Generation) — a VB.NET + Excel-DNA add-in distributed as a lightweight .xll.

Multiple Correspondence Analysis (CA) in Excel

Multiple Correspondence Analysis (MCA) is an exploratory multivariate technique designed to analyze and visualize relationships among several categorical variables. It can be seen as the extension of Correspondence Analysis (CA), which is limited to two categorical variables, to the case of more than two variables.