Statistics Playlist
Comprehensive journey through statistics for data science, from basics to advanced concepts, with hands-on Python implementation over 7 intensive sessions.
Pre-Algebra Video Playlist
Comprehensive pre-algebra video series covering essential topics from basic arithmetic to geometry, with clear explanations and numerous examples for effective learning.
Using R
Learn advanced statistical techniques and data analysis using R, covering distributions, simulations, estimations, and various statistical tests through practical examples and hands-on coding.
Statistical Theory
Explore advanced statistical concepts, proofs, and applications using R, covering topics from probability distributions to matrix properties and confidence intervals.
Survival Analyses
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.
Actuary Exams - Probability and Statistics
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.
Nonparametric Theory
Explore advanced nonparametric statistical methods, including sign tests, rank correlations, and order statistics, with practical applications and theoretical proofs.
Probability Measure
Explore set theory, fields, sigma fields, measurable spaces, and probability measures. Delve into conditional probability, independence, random variables, and distribution functions.
EM Algorithm
Explore the Expectation-Maximization (EM) algorithm through theory and practical examples, covering various statistical distributions and applications in data analysis.
Combinatorial Results
Explore combinatorial principles and their applications in probability, including dice games, card probabilities, and lottery analysis, with practical examples and derivations.
Mean, Variance, Moments, Mode of Distributions
Comprehensive exploration of statistical distributions, covering mean, variance, moments, and modes for various probability models, with practical applications in estimation and analysis.
Characteristic, Moment Generating, Factorial Generating Functions
Explore generating functions in probability theory, including characteristic, moment, and factorial, with applications to various distributions and statistical problems.
Basic Limit Theorems
Explore fundamental limit theorems in probability and statistics, including convergence modes, central limit theorem, and law of large numbers, with practical examples and proofs.
Transformations of Random Variables
Explore advanced techniques for transforming random variables, covering discrete and continuous cases, univariate and multivariate scenarios, and applications in statistical analysis.
Maximum Likelihood Estimation
Comprehensive exploration of Maximum Likelihood Estimation across various probability distributions, including practical applications using R for data generation and parameter estimation.
Approximations
Explore statistical approximations, including binomial, Poisson, and normal distributions. Learn delta method, confidence intervals, and moment estimation techniques for various probability distributions.
Fourier Series
Explore advanced mathematical concepts including Fourier series, orthogonal bases, and convergence theorems. Gain deep insights into complex analysis and integration techniques.
Exponential Family
Comprehensive exploration of exponential family distributions, covering theoretical aspects and practical applications using R for simulation and confirmation of results.
Quadratic Forms
Explore the distribution, properties, and independence of quadratic forms in multivariate normal distributions, including Cochran's Theorem and related proofs.
Multivariate
Explore advanced multivariate statistical concepts, including hypothesis testing, probability distributions, and data analysis techniques, with practical applications using R programming.
Generalized Linear Models
Comprehensive exploration of Generalized Linear Models, covering various regression techniques including Probit, Logistic, Complementary Log-Log, Poisson, Ordinal Logistic, and Multinomial Logistic.