Network Averaging: Theory and Applications

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Related lectures (29)

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Linear Independence: Covariance and Statistical Dependence

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Statistical Analysis of Network Data: Noisy Sampled Networks

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Statistics Essentials: The t-test

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Statistical Hypothesis Testing

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Likelihood Ratio Tests: Optimality and Extensions

Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.

Exploratory Statistics: Data Analysis Fundamentals

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.

Principal Component Analysis: Properties and Applications

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Understanding Statistics & Experimental Design

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.