Embark on your journey to mastering Hypothesis Testing with Python in this comprehensive course. It thoroughly covers how to conduct a variety of statistical tests, analyze and interpret results, enabling you to make data-driven decisions and inferences.
Overview
Syllabus
- Lesson 1: Understanding and Implementing Hypothesis Testing and T-tests with Python
- Evaluating Impact on Meeting Hours with T-Test
- Alter Sample Mean in Hypothesis Testing
- Performing the Two-Sample T-test on Working Hours Data
- Adding the Test: T-Statistic and P-Value Exploration
- Lesson 2: Mastering the Mann-Whitney U Test: Theory and Practice with Python
- Time Analysis with Mann-Whitney U Test
- Enhancing Output with Conditional Messaging
- Evaluate Web Layouts with Mann-Whitney U Test
- Web Analytics: Uncovering User Behavior with the Mann-Whitney U Test
- Lesson 3: Understanding and Implementing Analysis of Variance (ANOVA) with Python
- Weighing the Evidence with One-way ANOVA
- Incorporating New Apple Weights in ANOVA Analysis
- Weighing the Differences: ANOVA in Space
- Lesson 4: Cracking the Code with Chi-Square: Candy Colors and Neighborhoods in Python
- Cosmic Candy Color Choices: Unveiling Preferences with Chi-Square Test
- Candy Color Preferences: Adjusting Expectations
- Chi-Square Test: Matching Observed to Expected Frequencies
- Uncover the Preference Pattern with Chi-Square
