Hosted by Weijing Wang @ NYCU · 🇺🇸 US · EN · 13 episodes
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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.
Weijing Wang @ NYCU hosts Statistical Methods & Thinking, a education show with 13 episodes published.
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 experience
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In this episode, we step into multivariate thinking and ask a practical question: when do data points naturally form “groups,” and how can we use those groups to make decisions? We walk through how grouping methods decid
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This episode is about what to do when your data has many variables at once . We start with the basic idea of how variables “move together” (correlation and covariance), and why that matters for understanding patterns in
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This episode is about working with categorical outcomes —questions where results fall into categories rather than a numeric scale. We learn how to check whether two variables are related, how to model the chance of a “ye
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In this episode, we start with Fisher’s “Lady Tasting Tea” —a classic reminder that good questions need good experimental design. Then we shift from continuous outcomes to categorical data : how a simple 2×2 table turns
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This episode moves from one-way ANOVA to two-factor randomized experiments , focusing on how to test main effects and, more importantly, interactions —when the effect of one factor depends on the level of the other. Usin
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This episode introduces the core logic of experimental design and ANOVA: what we mean by causality, factors, and confounders—and why randomization, replication, and blocking are the practical tools that make comparisons
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Episode 6 is about making multiple regression work in real life : how to choose predictors without overfitting, when to transform variables to fix messy variance or nonlinearity, and what to do when predictors are strong
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Episode 5 connects the “big picture” of multiple linear regression: the matrix form of the model, how least squares and maximum likelihood lead to the same estimates under standard assumptions, and what the ANOVA table i
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Episode 4 introduces multiple linear regression —how to model an outcome using several predictors at once , and how to interpret each effect while holding the others constant . We cover dummy variables for categorical da
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This episode builds on simple linear regression by focusing on statistical inference —how we move from a fitted line to meaningful conclusions. We review the intuition behind least squares and explain why switching the r
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This episode introduces simple linear regression as a tool for understanding trends and making predictions from data. We begin with the historical insight of Francis Galton , whose study of the relationship between paren
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This episode is based on the course syllabus and the first lecture of Applied Statistical Methods . The primary goal is to introduce students to applied data analysis using statistical tools. The lecture focuses on the a
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Statistical Methods & Thinking is hosted by Weijing Wang @ NYCU. The show is categorised under education (courses) and has published 13 episodes.
Statistical Methods & Thinking has published 13 episodes.
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