Statistics Fundamentals
Comprehensive overview of statistical concepts, from basic distributions to advanced topics like maximum likelihood, providing a solid foundation for data analysis and interpretation.
Linear Regression and Linear Models
Learn powerful statistical tools: linear regression and models. Predict continuous values using various data types. Covers least squares, multiple regression, t-tests, ANOVA, and R implementation.
What Beer and Running Taught Me About the Scientific Process - Christie Aschwanden at SFI
Christie Aschwanden explores the scientific method through beer and running, revealing insights on statistics, methodology, and reproducibility while emphasizing the importance of skepticism in science journalism.
Introductory Hypothesis Testing
Learn essential concepts and techniques for statistical hypothesis testing, including p-values, errors, and various test types for means and proportions.
Statistics - Vol 5
Comprehensive guide to hypothesis testing, covering means, large samples, p-values, population proportions, and variances. Learn to calculate, interpret, and apply statistical concepts for robust data analysis.
Hypothesis Testing and P-Values
Learn key concepts in statistical inference, including p-values, hypothesis testing, t-tests, and ANOVA. Develop skills to choose appropriate tests and interpret results for data-driven decision making.
Choosing the Test, With Tests
Learn to select and apply statistical tests, interpret p-values, and perform analyses using Excel, covering t-tests, regression, and chi-square tests for various data types.
Excel Statistical Analysis - Hypothesis Testing - P-Value & Critical Value Methods - 1 Tail Upper
Master hypothesis testing in Excel: learn 5-step process, P-value and Critical Value methods for one-tail upper tests. Practical example using realtor salary data enhances understanding of statistical concepts.
Health Stats IQ
Explore key statistical concepts in epidemiology, including confounding variables, log scales, standardization, confidence intervals, p-values, and risk measures.
One-Way ANOVA - Analysis of Variance
Learn to perform and interpret one-way ANOVA, including formulas, examples, confidence intervals, and p-value calculations for comparing multiple group means.
Hypothesis Testing
Learn hypothesis testing fundamentals, including rejection regions, p-values, error types, power calculations, and statistical significance. Explore Z and t tests for one mean, with practical examples and assumptions.
Linear Regression, Clearly Explained
Clear explanation of linear regression concepts, including least squares, R-squared, multivariable models, p-values, and F-distribution. Ideal for beginners seeking a solid foundation in statistical analysis.
Practical Statistics for Data Scientists - Statistical Experiments Significance Testing
Learn statistical experiments and significance testing for data science, covering A/B testing, hypothesis testing, resampling, and ANOVA, with practical examples and insights.
Statistics in Medicine - Introduction to Clinical Research Analysis
Explore key statistical concepts in clinical research, including descriptive and inferential statistics, confidence intervals, p-values, and various statistical tests used in medical studies.
Numbers and Art: Statistical Pitfalls and Their Consequences
Explore statistical pitfalls in data analysis, from Simpson's Paradox to hypothesis testing. Learn to critically evaluate models and metrics, understanding their real-world implications beyond simple accuracy measures.
Controlling False Discoveries in Genomics Data Analysis - What is the Null Hypothesis?
Explore advanced techniques for controlling false discoveries in genomics data analysis, focusing on null hypothesis formulation and its impact on statistical inference in high-dimensional biological datasets.
Statistical Analysis in Cosmology: Goodness-of-Fit and Model Selection - Part 2
Explore advanced statistical methods in cosmology, including model selection, MCMC, chi-square, and p-value analysis for robust data interpretation.
Hypothesis Testing with t-Tests and p-Values - Spring 2023
Master statistical hypothesis testing through hands-on practice with t-tests and p-values, building essential skills for data-driven decision making and scientific research.
Statistics Introduction - Null Hypothesis, Odds Ratio, Confidence Interval, and P-value
Master essential statistical concepts including hypothesis testing, odds ratios, confidence intervals, and p-values for medical research and data analysis. Learn key terms and methods for interpreting clinical study results.