About this course

Statistics 2 Part 2 is a comprehensive course designed by the London School of Economics (LSE) focused on advancing your understanding of elementary statistical theory. This course delves deeper into measurement techniques, methods, and hypothesis testing concepts that were introduced in previous modules. It's suitable for either standalone study or as an installment in the LSE's MicroBachelors program in Statistics Fundamentals.

What students will learn

• Build upon key ideas from initial Statistics courses, tailored for those with moderate mathematical skills.
• Apply various methods to explain, summarize, present data, and interpret results accurately using well-structured diagrams, titles, and labels.
• Understand and apply statistical inference fundamentals to justify the use of an appropriate model and conduct tests in various contexts.
• Recognize that statistical techniques are assumption-based and evaluate the plausibility of these assumptions in practical scenarios.

Prerequisites

This intermediate-level course does not require previous knowledge in statistics, although prior completion of LSE's Statistics 1 and the first part of Statistics 2 is highly recommended to ensure a solid foundational understanding and to cope with the cumulative nature of the content covered.

Course Content

• Sampling distributions of statistics
• Point estimation I
• Point estimation II and interval estimation
• Hypothesis testing
• Analysis of variance (ANOVA)

Who this course is for

This course is ideal for individuals interested in econometrics, finance, and quantitative social sciences, and those looking to build a foundational knowledge in statistics for pursuing more specialized courses.

Application of Skills in the Real World

Skills acquired from this course can be applied in various real-world settings such as in economic analysis, financial modeling, and social science research, where rigorous quantitative analysis and statistics are essential.

Syllabus

• Sampling distributions of statistics
• Point estimation I
• Point estimation II and interval estimation
• Hypothesis testing
• Analysis of variance (ANOVA)