Hypothesis Testing with Python
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.
Lessons and practices
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
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.