Central Tendency and Dispersion

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Page 3 of 4

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Covers exploratory statistics, focusing on populations, samples, and various statistical measures.

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

Covers Analysis of Variance (ANOVA) for assessing categorical variable effects on quantitative outcomes.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

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

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

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Covers mean, median, mode, box plots, and histograms in datasets.