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Statistical Methods & Thinking

Statistical Methods & Thinking

Released: 2026-02-03
© Weijing Wang @ NYCU
Statistical Methods & Thinking - QR Code
13 Episodes
Audio
Listen on Apple Podcasts
13 Episodes
Audio
Listen on Apple Podcasts
Released: 2026-02-03
© Weijing Wang @ NYCU
Most Recent Episode
Episode 13 | Survival Analysis: Making Sense of Time-to-Event Data

Episode 13 | Survival Analysis: Making Sense of Time-to-Event Data

In this episode, we introduce the core ideas behind analyzing time-to-event data—situations where the outcome isn’t just “what happened,” but when it happened. A key challenge is that some participants haven’t experienced the event yet by the
Time: 41:36
In this episode, we introduce the core ideas behind analyzing time-to-event data—situations where the outcome isn’t just “what happened,” but when it happened.
A key challenge is that some participants haven’t experienced the event yet by the end of follow-up (or they drop out), so the data are only partially observed.
We build the intuition for describing how risk changes over time, then walk through three practical tools: how to estimate a survival curve from one group, how to compare two groups fairly over the whole follow-up, and how to study the role of multiple predictors while keeping the time dimension front and center.
Episode ID: 1000747885198
GUID: be2091b1-a5da-4f79-8bfc-cea004aa7cbe
Release Date: 2/3/2026, 11:06:28 AM

Description

The materials in this podcast are generated by NotebookLM based on the lecture notes of the course Applied Statistical Methods, offered at NYCU and taught by Weijing Wang.
The podcast covers core methods for analyzing associations in data, including correlation analysis, simple and multiple linear regression (estimation, testing, and model checking), and discussions on association versus causation. It also introduces methods for categorical data analysis such as contingency tables, chi-square tests, logistic regression, and the generalized linear model framework.

Apple Podcasts: Customer Reviews

2026-03-17

Love it!

Informative and fun, even if it’s AI narrated. Great content.
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