This Advanced Statistics course is designed for learners who already understand basic statistics and want to deepen their knowledge in probability theory and statistical inference. The course provides a structured introduction to advanced statistical methods used in research and data analysis.

It begins with a course overview and a recap of probability fundamentals, then moves into probability distribution families and their relationships. Learners will study probability distribution moments and learn how different distributions are analyzed and compared in statistical modeling.

The course also introduces probability distribution testing, helping learners understand how statistical models are validated.

In the second part of the course, the focus shifts to hypothesis testing involving two population means. Students will learn different testing scenarios including cases with known variances, unknown equal variances, and unknown unequal variances. The course also explains how to calculate p-values and construct confidence intervals for two-sample tests.

These topics are essential for advanced data analysis, research, and scientific decision-making.

This course is ideal for university students, researchers, and professionals in data science, engineering, economics, and analytics.

By the end of the course, learners will be able to perform advanced statistical tests, compare populations, and interpret results with confidence.