Experimental Design and Analysis

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Related lectures (32)

Page 2 of 4

Understanding Statistics & Experimental Design

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Heteroskedasticity: Ch. 4a

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.

Comparison Statistics: Hypothesis Testing and ANOVA

Explores comparison statistics, hypothesis testing, ANOVA, correlation, and linear regression.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Supervised Learning with kNN: Regression Model

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Statistical Mediation and Moderation

Explores statistical mediation, moderation, ANOVA, and hypothesis testing.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.