Causal Inference: Understanding Associations and Effects

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Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers the analysis of chimpanzee data on learning times and explores generalized linear models and logistic regression.

Explores statistical dispersion and its impact on determining normal values and data analysis.

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.