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Statistics 2 Part 2

Statistical Inference

James Abdey

Statistics 2 Part 2 is a self-paced course from LSE which aims to develop your knowledge of elementary statistical theory, particularly relating to the concepts, methods and techniques of measurement and hypothesis testing that were introduced in Statistics 1 and Statistics 2, Part 1. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.

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Statistics 2 Part 2 is a self-paced course from LSE which aims to develop your knowledge of elementary statistical theory, particularly relating to the concepts, methods and techniques of measurement and hypothesis testing that were introduced in Statistics 1 and Statistics 2, Part 1. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.

Part 2, Statistical Inference, covers the following topics:

● Sampling distributions of statistics

● Point estimation I

● Point estimation II and interval estimation

● Hypothesis testing

● Analysis of variance (ANOVA)

There is an emphasis on topics that relate to econometrics, finance and quantitative social science. Concepts and methods that provide the foundations for more specialised courses in statistics and econometrics are introduced.

What you'll learn

By the end of this course, you will:

  • Have developed key ideas from Statistics 1 that are accessible to a student with a moderate mathematical competence

  • be able to routinely apply a variety of methods for explaining, summarising and presenting data and interpreting results clearly using appropriate diagrams, titles and labels when required

  • explain the fundamentals of statistical inference and apply these principles to justify the use of an appropriate model and perform tests in a number of different settings

  • demonstrate understanding that statistical techniques are based on assumptions and the plausibility of such assumptions must be investigated when analysing real problems.

What's inside

Syllabus

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

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by James Abdey, a well-known instructor in Statistics, this course will be beneficial to learners looking to fill gaps in their foundational knowledge
Focuses on elementary statistical theory, particularly relating to the concepts, methods and techniques of measurement and hypothesis testing
Provides a solid foundation for more specialized courses in statistics and econometrics
Places an emphasis on sampling distributions of statistics, point estimation, and hypothesis testing
Requires learners to come in with moderate mathematical competence, making it a suitable course for learners with some prior knowledge in Statistics

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Activities

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Career center

Learners who complete Statistics 2 Part 2: Statistical Inference will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use statistical methods to analyze data and build models that can help businesses make informed decisions. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Econometrician
Econometricians use statistical methods to analyze economic data and make forecasts. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Biostatistician
Biostatisticians use statistical methods to design and analyze studies in the biomedical field. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Statistician
Statisticians apply statistical methods to collect, analyze, interpret, and present data. This course can help you develop the skills and knowledge you need to succeed in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze financial data and make investment decisions. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make investment recommendations. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Actuary
Actuaries use statistical methods to assess and manage financial risk. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Epidemiologist
Epidemiologists use statistical methods to study the causes and distribution of disease. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Data Analyst
Data Analysts interpret and communicate data to help organizations make informed decisions. This course, Statistics 2 Part 2: Statistical Inference, can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn how to analyze data, draw conclusions, and communicate your findings effectively.
Survey Researcher
Survey Researchers design and conduct surveys to collect data about a population. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Operations Research Analyst
Operations Research Analysts use statistical methods to improve the efficiency of business operations. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Business Analyst
Business Analysts use statistical methods to analyze data and identify trends and patterns that can help businesses improve their operations. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Social Scientist
Social Scientists use statistical methods to study human behavior and society. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Risk Analyst
Risk Analysts use statistical methods to assess and manage risk. This course can help you build a foundation in statistical theory and methods, which are essential for success in this role. You will learn about sampling distributions, point estimation, interval estimation, hypothesis testing, and analysis of variance.
Market Researcher
Market Researchers conduct surveys, collect data, and analyze market trends to help businesses make informed decisions. This course can help you build a foundation in statistical methods, which are essential for success in this role. You will learn how to design and conduct surveys, analyze data, and draw conclusions.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Statistics 2 Part 2: Statistical Inference.
Is useful as a reference for many of the techniques introduced in this course. It provides conceptually clear explanations along with detailed mathematical proofs.
Commonly-used textbook for mathematical statistics, which covers many of the topics that are introduced in this course in greater depth.
Provides a broad overview of the foundations of econometrics, which field that heavily utilizes statistical methods. It useful supplement for students who want to position their statistical knowledge within the broader context of econometrics.
Provides an overview of how statistical methods are used to understand psychological research. It is particularly useful for students who are interested in studying or practicing psychology after completing this course.
Provides a framework for causal inference, which related field that uses statistical methods to determine the causal effects of different interventions or treatments.
Covers many of the topics covered in this course, but with a focus on Bayesian statistical methods.
Provides an accessible introduction to Bayesian statistical methods, which are becoming increasingly popular in various fields.
Covers many advanced topics in statistical learning, including machine learning methods. It great resource for students who want to pursue further study in this area.
Covers advanced topics in machine learning, such as probabilistic graphical models. It good reference for students who want to specialize in the application of statistical methods to machine learning.
Covers reinforcement learning, which subfield of machine learning that is concerned with how an agent can learn to make optimal decisions in a complex environment.
Provides a comprehensive overview of deep learning, which subfield of machine learning that has become increasingly popular in recent years.

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