Introductory Hypothesis Testing
Learn essential concepts and techniques for statistical hypothesis testing, including p-values, errors, and various test types for means and proportions.
Bootstrap Confidence Intervals
Learn to create and interpret bootstrap confidence intervals for various statistical measures, enhancing your data analysis skills.
Maximum Likelihood Estimation
Explore maximum likelihood estimation through examples of binomial, Poisson, and uniform distributions, enhancing statistical analysis skills.
Multiple Random Variables - Joint and Conditional Distributions
Explore joint and conditional distributions of multiple random variables, covering discrete and continuous cases, CDFs, independence, covariance, and variance of sums.
Discrete Random Variables
Explore discrete random variables, including binomial, hypergeometric, and Poisson distributions. Learn probability mass functions, expected values, and variance in this comprehensive introduction.
Linear Regression Essentials
Comprehensive overview of linear regression, covering simple and multiple regression, matrix notation, estimator properties, inference, and model evaluation metrics.
Statistics - Vol 3
Comprehensive exploration of sampling distributions, Central Limit Theorem, and confidence intervals. Covers statistical concepts for estimating population means and proportions using various sample sizes.
Statistics - Vol 8
Comprehensive exploration of statistical concepts including scatter plots, correlation, linear regression, and multiple regression, with practical Excel applications and in-depth analysis techniques.
Random Variables
Explore continuous random variables, probability density functions, and the normal distribution. Learn to calculate expected values, variances, and use statistical tables for data analysis.
Bivariate Data Analysis
Explore scatter plots, correlation coefficients, and lines of best fit to analyze relationships between two variables in this comprehensive introduction to bivariate data analysis.
Basic Single Variable Analysis
Explore misleading graphs, means, medians, and statistical measures through engaging problems and visual representations.
Learn Statistics with Python
Master Python for statistical analysis: design matrices, linear models, F distribution, and OLS. Hands-on tutorials using Orange and Patsy libraries for practical data science skills.
The Julia Computer Language
Learn Julia programming language fundamentals, data structures, control flow, functions, and data visualization with Gadfly. Practical medical statistics project included.
Learning Medical Statistics with Python and Jupyter Notebooks
Learn medical statistics using Python and Jupyter notebooks. Covers common tests, data analysis, and visualization techniques. Ideal for healthcare professionals seeking practical skills in statistical analysis.
Healthcare Statistics with SPSS
Learn to analyze healthcare data using SPSS, covering data creation, import, visualization, descriptive statistics, file manipulation, and parametric testing.
Julia
Learn Julia programming for data analysis, statistics, and linear algebra. Covers databases, plotting, environments, and advanced mathematical concepts with practical tutorials.
Data Viz Using Plotly for R
Learn to create interactive data visualizations with Plotly in R, covering various chart types and customization techniques for effective data presentation.
R Tutorials
Learn essential R skills for data analysis, visualization, and machine learning, covering statistics, deep learning, Plotly, and real-world applications like COVID-19 data extraction.
Web Scraping and Covid 19
Learn web scraping techniques to gather COVID-19 data, with practical examples and ethical considerations for responsible data collection and analysis.
Ordinal Logistic Regression
Learn to analyze ordered categorical data using ordinal logistic regression, covering introduction, multiple regression, and the proportional odds assumption.
Social Science Methods & Automated Data Algorithms
Explore cutting-edge data algorithms in social science, covering AI ethics, privacy implications, and innovative applications like deep learning for urban analysis.