PODCAST · education
Statistical Methods & Thinking
by Weijing Wang @ NYCU
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.
Episodes
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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 end of follow-up (or they drop out), so the data are...
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Episode 12 | Clustering and Classification: Finding Structure in Data
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 decide what’s “close” or “similar,” then compare two main approaches—building...
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Episode 11 | Finding Structure in Multivariate Data
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 real datasets. Then we introduce dimension reduction—ways to compress lots of...
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Episode 10 | From Chi-Square to GLMs: Beyond Linear Regression
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 “yes/no” outcome using multiple predictors, and how to compare different modeling...
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Episode 9 | Categorical Data in Practice: Measures of Association, and Simpson’s Paradox
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 test results into sensitivity/specificity, and study results into association...
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Episode 8 | Two-Way ANOVA and Beyond
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. Using examples like printer sales and a fish reproduction index, we show how ANOVA...
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Episode 7 | Design of Experiments
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 fair. We build the one-way ANOVA model, run the hypothesis test in R, and...
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Episode 6 | Model Selection Strategies
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 strongly correlated. We’ll walk through tools like Mallows’ Cp, partial F tests, and...
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Episode 5 | Deeper in Multiple Linear Regression
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 is really decomposing. We compare r-square vs. adjusted r-square, review…
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Episode 4｜Multiple Linear Regression
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 data, and interaction terms (e.g., how experience and gender together can change...
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Episode 3｜Association, Inference, and Causal Thinking in Simple Linear Regression
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 roles of the two variables can lead to different fitted lines. We then discuss...
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Episode 2｜Simple Linear Regression
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 parents’ and children’s heights helped popularize the idea of describing…
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Episode 1｜Seeing Association in Data: Scatter Plots and Correlation
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 analysis of association in data, with particular emphasis on graphical methods...
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ABOUT THIS SHOW
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.
HOSTED BY
Weijing Wang @ NYCU
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Frequently Asked Questions
How many episodes does Statistical Methods & Thinking have?
Statistical Methods & Thinking currently has 13 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Statistical Methods & Thinking about?
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...
How often does Statistical Methods & Thinking release new episodes?
Statistical Methods & Thinking has 13 episodes. Check the episode list to see recent publication dates and frequency.
Where can I listen to Statistical Methods & Thinking?
You can listen to Statistical Methods & Thinking on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Statistical Methods & Thinking?
Statistical Methods & Thinking is created and hosted by Weijing Wang @ NYCU.
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