Lectures related to Statistics and visualisation of morphometric data

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Explores model selection in statistics, discussing principles, probabilistic models, characteristics evaluation, and data visualization methods.

Explores probability theory, distribution of averages, and the central limit theorem.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores variance, covariance, and practical applications in statistics and probability.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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

Explains statistical methods in quantitative analysis, emphasizing precision, accuracy, and sample representation.

Covers the review of random variables, probability density functions, variance, and Gaussian processes.

Explores replicates, visualization methods, central tendency measures, outliers, dispersion, averages, residuals, and unbiased estimators.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

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

Introduces probability theory, random variables, and distributions, with a focus on their applications in atomic diffusion.

Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

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

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.