Most Important Statistical Tests in R скачать в хорошем качестве

Most Important Statistical Tests in R

Explore the world of statistical analysis with this clear breakdown of the most common parametric tests in R. Whether you're a beginner in R or brushing up on statistical methods, this video shows you when to use each test, how to run it in R, and how to interpret the results — with real examples and practical guidance. From t-tests and ANOVA to regression models, correlation, and proportion tests — this video walks you through the assumptions behind each method and how to avoid common pitfalls. 🧠 What you’ll learn: Key questions statistical tests answer How to understand and interpret p-values, confidence intervals, and model coefficients Using real datasets to uncover effects and relationships Visual tips for checking normality, spotting outliers, and reading regression output ⏱ Time Stamps ⌚ 0:00 - Intro & Hypothesis Testing Overview 1:27 - T-Tests (One-sample, Two-sample, Paired) 9:20 - ANOVA (plus Levene's test, Tukey HSD) 15:38 - Correlation (cor, cor.test, corr.test) 19:32 - Linear Regression (lm, predict, expand.grid) 28:03 - Logistic Regression (glm, log-odds, pseudo R²) 33:31 - Proportion Tests (prop.test, binom.test) 38:02 - Chi-Square Test of Independence (plus Fisher’s Exact) 41:58 - Normality Test (shapiro_test) 👨‍🏫 With over 15 years of experience using R for research and business analytics, I guide you through practical tools for better data decisions. 🎯 Boost your confidence in data analysis, and deepen your understanding of statistical assumptions that drive valid conclusions. skool.com/data-analysis-with-r-6607