In this video, we dive into Part I of a two part video series on Hypothesis Testing in R using powerful libraries like tidyverse and broom. Support me: BuyMeACoffee: Patreon: Ko-fi: Follow me: Twitter: Github: Topics Covered: 1. One sample tests 2. z-score and z distribution (standard normal distribution) 3. Bootstrap Distribution 4. Two sample tests (paired and unpaired) 5. t-test and t-distribution 6. p-values and significance 7. Setting up hypothesis 8. Testing hypothesis 9. Analysis of Variance (ANOVA) Chapters: 0:00 Introduction 1:22 One Sample Tests 3:00 Bootstrap Distribution 4:37 Standard Deviation and Standard Error 5:04 z-score 6:15 z-distribution/ Standard Normal Distribution 6:47 Null Hypothesis and Alternative Hypothesis 9:36 Concepts of t-tests and p-values 12:13 Concepts of statistical significance 14:13 Type I and Type II Errors and Confusion Matrix 16:20 Two Sample Tests 19:06 t-test statistics 21:03 t-distribution 22:39 Degrees of Freedom 23:09 Unpaired t-tests 25:03 Paired t-tests 26:58 ANOVA (Analysis of Variance) 29:00 Pairwise tests Datasets: Salaries data: Modified Salaries data: In-depth videos on: Linear Regression with Tidyverse (R): Linear Regression with statmodels (Python): Supervised Learning with scikit-learn (Python) Part I: Supervised Learning with scikit-learn (Python) Part II: Don’t forget to Like, Share, and Subscribe for more data science content! #rstats #dataanalysis