Quantitative Analysis: Statistical Methods

Related lectures (31)

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

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

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

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

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

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

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

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

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

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