Significant Figures, Error Estimation, Notation

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Explores random variables, their variability, realization of random processes, and the scientific method in material science.

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

Explores partial derivatives and derivability of functions, emphasizing geometric interpretations and avoiding common pitfalls.

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

Covers statistical analysis and visualization of morphometric data, including discrimination between tree types and numpy calculations.

Explains data dispersion, central tendency, variance, standard deviation, and Mean Square Error.

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

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

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

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.