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Comprehensive exploration of advanced statistical distributions, covering derivations, properties, and applications of various probability models for in-depth understanding and analysis.
Explore correlation concepts, from Pearson's coefficient to advanced topics like partial and rank correlations. Learn geometrical interpretations, multiple regression, and practical applications using R.
Learn to calculate power and sample size for various statistical tests in R, from t-tests to ANOVA and multivariate analyses, enhancing your ability to design robust studies.
Learn advanced statistical techniques and data analysis using R, covering distributions, simulations, estimations, and various statistical tests through practical examples and hands-on coding.
Explore advanced statistical concepts, proofs, and applications using R, covering topics from probability distributions to matrix properties and confidence intervals.
Learn to generate censored exponential data, derive density and likelihood for censored data, and understand the Kaplan-Meier estimator as a maximum likelihood estimator in R.
Comprehensive preparation for actuarial probability and statistics exams, featuring 8 full-length practice tests divided into manageable segments, with additional sample problems for in-depth study.
Explore essential pre-calculus concepts including function transformation, inverse functions, quadratic functions, and polynomials to build a strong mathematical foundation.
Explore key eigenvalue inequalities for symmetric matrices, including Courant-Fischer, Weyl's, Gershgorin Circle, and Brauer's oval theorems, with applications to correlation matrices.
Explore advanced nonparametric statistical methods, including sign tests, rank correlations, and order statistics, with practical applications and theoretical proofs.
Explore set theory, fields, sigma fields, measurable spaces, and probability measures. Delve into conditional probability, independence, random variables, and distribution functions.
Explore the Expectation-Maximization (EM) algorithm through theory and practical examples, covering various statistical distributions and applications in data analysis.
Explore combinatorial principles and their applications in probability, including dice games, card probabilities, and lottery analysis, with practical examples and derivations.
Comprehensive exploration of statistical distributions, covering mean, variance, moments, and modes for various probability models, with practical applications in estimation and analysis.
Explore generating functions in probability theory, including characteristic, moment, and factorial, with applications to various distributions and statistical problems.
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