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James Abdey
Statistics 2 Part 1 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, Parts 1 and 2. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 1, Probability and Distribution Theory, covers the following topics:
● Probability theory I
● Probability theory II
● Random variables
● Common distributions of random variables
● Multivariate random variables
Read more
Statistics 2 Part 1 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, Parts 1 and 2. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 1, Probability and Distribution Theory, covers the following topics:
● Probability theory I
● Probability theory II
● Random variables
● Common distributions of random variables
● Multivariate random variables
Read more
Statistics 2 Part 1 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, Parts 1 and 2. This course can be taken alone or as part of the LSE MicroBachelors program in Statistics Fundamentals.
Part 1, Probability and Distribution Theory, covers the following topics:
● Probability theory I
● Probability theory II
● Random variables
● Common distributions of random variables
● Multivariate random variables
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 undergraduate-level 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
-
apply and be competent users of standard statistical operators
-
be able to recall a variety of well-known distributions and their respective moments
Register for this course and see more details by visiting:
OpenCourser.com/course/fbmmc5/statistics
What's inside
Syllabus
● Probability theory I
● Probability theory II
● Random variables
● Common distributions of random variables
Read more
Syllabus
● Probability theory I
● Probability theory II
● Random variables
● Common distributions of random variables
Read more
● Multivariate random variables
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces foundational statistical methods and concepts that are building blocks for other Statistics courses
Develops learners' ability to analyze data, which is applicable across many sectors and disciplines
Covers key statistical ideas from Statistics 1 to strengthen learners' existing understanding
Focuses on probability and distribution theory, providing a comprehensive study of foundational statistics
Emphasizes topics that are particularly relevant to econometrics, finance, and quantitative social science
Taught by instructors from LSE, who are highly respected in the field of statistics
Save this course
Save Statistics 2 Part 1: Probability and Distribution Theory to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Statistics 2 Part 1: Probability and Distribution Theory with these
activities:
Review Calculus and Linear Algebra
Show steps
Refreshing your knowledge of calculus and linear algebra will help you better understand the mathematical concepts used in statistics.
Browse courses on
Calculus
Show steps
-
Review your notes or textbooks from previous calculus and linear algebra courses.
-
Work through practice problems to test your understanding.
-
Seek help from a tutor or online resources if needed.
Identify a Mentor in Statistics
Show steps
Finding a mentor can provide guidance and support throughout your statistical journey.
Browse courses on
Mentorship
Show steps
-
Attend statistics-related events and conferences.
-
Reach out to professors, researchers, or industry professionals in the field.
-
Ask for recommendations from classmates or colleagues.
Read 'Mathematical Statistics' by Hogg and Craig
Show steps
This textbook provides a comprehensive overview of mathematical statistics and will supplement the course material effectively.
View
Introduction to Mathematical Statistics, Global...
on Amazon
Show steps
-
Read the assigned chapters thoroughly.
-
Take notes and highlight important concepts.
-
Work through the practice problems at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize and Review Course Materials
Show steps
By organizing and regularly reviewing your course materials, you can reinforce learning and identify areas for improvement.
Show steps
-
Create a system for organizing notes, assignments, and other course materials.
-
Set aside time each week to review your materials.
-
Identify areas where you need additional support or clarification.
-
Seek help from your instructor or classmates if needed.
Practice Probability Theory
Show steps
Solving practice problems will help you reinforce your understanding of the concepts and techniques of probability theory.
Browse courses on
Probability Theory
Show steps
-
Find practice problems online or in textbooks.
-
Set aside time each week to work on practice problems.
-
Check your answers against the provided solutions or ask for help from a tutor or classmate if needed.
Create a Probability Distribution Chart
Show steps
Creating a visual representation of a probability distribution will help you understand the shape and characteristics of the distribution.
Browse courses on
Probability Distributions
Show steps
-
Choose a random variable and its probability distribution.
-
Calculate the probabilities for different values of the random variable.
-
Create a chart or graph to plot the probabilities.
Attend a Statistics Workshop or Seminar
Show steps
Attending workshops or seminars can provide exposure to new statistical techniques and applications.
Browse courses on
Professional Development
Show steps
-
Research upcoming statistics workshops or seminars in your area.
-
Select a workshop or seminar that aligns with your interests and learning goals.
-
Register and attend the workshop or seminar.
-
Actively participate in the workshop or seminar.
Develop a Statistical Model for a Real-World Problem
Show steps
Applying statistical concepts to solve real-world problems will enhance your problem-solving skills and deepen your understanding of the practical applications of statistics.
Browse courses on
Hypothesis Testing
Show steps
-
Identify a real-world problem that can be addressed using statistical methods.
-
Gather data relevant to the problem.
-
Formulate a statistical hypothesis and choose appropriate statistical tests.
-
Conduct the statistical analysis and interpret the results.
-
Present your findings and recommendations.
Review Calculus and Linear Algebra
Show steps
Refreshing your knowledge of calculus and linear algebra will help you better understand the mathematical concepts used in statistics.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review your notes or textbooks from previous calculus and linear algebra courses.
- Work through practice problems to test your understanding.
- Seek help from a tutor or online resources if needed.
Identify a Mentor in Statistics
Show steps
Finding a mentor can provide guidance and support throughout your statistical journey.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Attend statistics-related events and conferences.
- Reach out to professors, researchers, or industry professionals in the field.
- Ask for recommendations from classmates or colleagues.
Read 'Mathematical Statistics' by Hogg and Craig
Show steps
This textbook provides a comprehensive overview of mathematical statistics and will supplement the course material effectively.
View
Introduction to Mathematical Statistics, Global...
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters thoroughly.
- Take notes and highlight important concepts.
- Work through the practice problems at the end of each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize and Review Course Materials
Show steps
By organizing and regularly reviewing your course materials, you can reinforce learning and identify areas for improvement.
Show steps
Show steps
Show steps
- Create a system for organizing notes, assignments, and other course materials.
- Set aside time each week to review your materials.
- Identify areas where you need additional support or clarification.
- Seek help from your instructor or classmates if needed.
Practice Probability Theory
Show steps
Solving practice problems will help you reinforce your understanding of the concepts and techniques of probability theory.
Browse courses on
Probability Theory
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Set aside time each week to work on practice problems.
- Check your answers against the provided solutions or ask for help from a tutor or classmate if needed.
Create a Probability Distribution Chart
Show steps
Creating a visual representation of a probability distribution will help you understand the shape and characteristics of the distribution.
Browse courses on
Probability Distributions
Show steps
Show steps
Show steps
- Choose a random variable and its probability distribution.
- Calculate the probabilities for different values of the random variable.
- Create a chart or graph to plot the probabilities.
Attend a Statistics Workshop or Seminar
Show steps
Attending workshops or seminars can provide exposure to new statistical techniques and applications.
Browse courses on
Professional Development
Show steps
Show steps
Show steps
- Research upcoming statistics workshops or seminars in your area.
- Select a workshop or seminar that aligns with your interests and learning goals.
- Register and attend the workshop or seminar.
- Actively participate in the workshop or seminar.
Develop a Statistical Model for a Real-World Problem
Show steps
Applying statistical concepts to solve real-world problems will enhance your problem-solving skills and deepen your understanding of the practical applications of statistics.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using statistical methods.
- Gather data relevant to the problem.
- Formulate a statistical hypothesis and choose appropriate statistical tests.
- Conduct the statistical analysis and interpret the results.
- Present your findings and recommendations.
Career center
Learners who complete Statistics 2 Part 1: Probability and Distribution Theory will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians apply statistical techniques to solve real-world problems in various fields. Statistics 2, with its focus on probability and distribution theory, forms the cornerstone of statistical practice. It provides a deep understanding of the principles of probability, random variables, and distributions, enabling you to design and conduct statistical studies, interpret data, and draw meaningful conclusions. This course is highly recommended for individuals seeking a career in statistics, as it provides a strong foundation for further specialization in fields such as biostatistics, econometrics, and data science.
Data Analyst
Data Analyst
Data Analysts provide crucial insights by collecting and cleaning data, uncovering patterns, and driving actionable insights. Statistics 2, with its emphasis on probability theory and distribution theory, equips you with the foundation to master statistical tools and methodologies. Through this course, you will develop an understanding of probability distributions, random variables, and multivariate random variables, enabling you to extract meaningful insights from complex data, a highly sought-after skill in the data analytics domain.
Data Scientist
Data Scientist
Data Scientists are in high demand for their ability to extract insights from vast amounts of data. Statistics 2, with its focus on probability and distribution theory, provides a foundational understanding of the statistical principles underlying data analysis. Through this course, you will learn to model and analyze data, uncover patterns, and make informed decisions. The emphasis on multivariate random variables will equip you to handle complex datasets and develop robust predictive models, a key skill for Data Scientists.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for evaluating financial data and making investment recommendations. The course Statistics 2 provides a solid foundation in probability and distribution theory, enhancing your ability to understand and analyze financial data, assess risks, and make informed investment decisions. By gaining mastery over concepts like random variables and probability distributions, you will be well-equipped to forecast financial trends, identify investment opportunities, and manage financial portfolios effectively.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data and test economic theories. Statistics 2, with its focus on probability and distribution theory, builds a solid foundation for econometric modeling. It provides you with the tools to understand and apply statistical techniques to economic data, enabling you to evaluate economic relationships, forecast economic indicators, and make informed policy decisions. The emphasis on multivariate random variables will equip you to handle complex economic models and analyze interconnected economic variables.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to analyze biological and medical data. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for biostatistical modeling. It equips you with the tools to understand and apply statistical techniques to medical research, clinical trials, and public health studies. The emphasis on multivariate random variables will enable you to handle complex biological data and develop robust statistical models to advance medical knowledge and improve healthcare outcomes.
Market Research Analyst
Market Research Analyst
Market Research Analysts play a critical role in understanding consumer behavior and market trends. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for this role. It equips you with the skills to gather, analyze, and interpret data, enabling you to understand consumer preferences, market dynamics, and industry trends. The course's emphasis on multivariate random variables will enhance your ability to model complex market scenarios and make data-driven recommendations to drive business decisions.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers apply mathematical and statistical techniques to analyze financial markets and develop trading strategies. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for quantitative research in finance. It equips you with the tools to understand and apply statistical techniques to financial data, model market behavior, and develop investment strategies. The emphasis on multivariate random variables will enable you to handle complex financial data and develop robust quantitative models to navigate financial markets effectively.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning algorithms to solve complex problems. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for understanding and applying statistical principles in machine learning. It equips you with the tools to develop and evaluate machine learning models, handle uncertainty in data, and make informed decisions based on model predictions. The emphasis on multivariate random variables will enable you to handle complex data and develop robust machine learning models in various domains.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risks in the insurance and finance industries. Statistics 2, with its focus on probability and distribution theory, builds a strong foundation for actuarial practice. It provides you with the tools to understand and apply statistical techniques to insurance and financial data, enabling you to evaluate risks, set insurance premiums, and develop financial products. The emphasis on multivariate random variables will equip you to handle complex risk scenarios and develop robust actuarial models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are highly sought after in the finance industry for their expertise in mathematical and statistical modeling. Statistics 2 offers a solid foundation in probability and distribution theory, which are essential for developing and applying quantitative models in finance. Through this course, you will gain proficiency in analyzing and interpreting financial data, building risk models, and making informed investment decisions. The course's focus on multivariate random variables will equip you to handle complex financial scenarios and develop robust quantitative models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply mathematical and statistical techniques to improve the efficiency and effectiveness of organizations. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for understanding and applying statistical models in operations research. Through this course, you will develop the skills to analyze operational data, optimize processes, and make data-driven decisions to enhance organizational performance. The emphasis on multivariate random variables will equip you to model complex operational systems and develop robust solutions.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and mitigating risks within organizations. Statistics 2, with its focus on probability and distribution theory, provides a strong foundation for understanding and quantifying risks. The course equips you with the tools to analyze risk data, assess probabilities, and develop strategies to manage and mitigate risks effectively. The emphasis on multivariate random variables will enhance your ability to model complex risk scenarios and make informed decisions to safeguard organizations from potential threats.
Data Engineer
Data Engineer
Data Engineers design and manage data pipelines to ensure the availability, reliability, and security of data. Statistics 2, with its focus on probability and distribution theory, provides a foundation for understanding data characteristics and developing robust data management strategies. It equips you with the tools to assess data quality, handle uncertain data, and design data pipelines that can adapt to changing data patterns. The emphasis on multivariate random variables will enable you to model complex data relationships and develop scalable data management solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Statistics 2, with its focus on probability and distribution theory, may be useful for Software Engineers who work on developing machine learning or data-driven applications. It provides a foundation for understanding and applying statistical techniques in software development, enabling you to build software that can handle uncertain data, make probabilistic predictions, and adapt to changing user behavior. The emphasis on multivariate random variables will equip you to model complex user interactions and develop robust software systems.
For more career information including salaries, visit:
OpenCourser.com/course/fbmmc5/statistics
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 1: Probability and Distribution Theory.
Save
This open-source statistics textbook is available online for free. It contains helpful practice problems, exercises, and examples that are good for Statistics 2 learners at all levels.
This popular statistics textbook is commonly used in high schools and introductory college courses. It is appropriate for Statistics 2 learners who want to strengthen their foundation in statistical theory.
This text commonly-used, introductory statistics text for psychology students. It is an appropriate reference for any Statistics 2 learner.
Mathematical Statistics with Applications provides an ample supply of practice problems and examples that should be helpful to any learner in Statistics 2.
This introduction to probability and statistics is recommend as a general reference source to complement the Statistics 2 course. It is appropriate for most learners, especially those interested in additional background information.
This text has an engineering focus and could be a good reference for Statistics 2 learners who are interested in the applications of probability to electrical engineering.
As a comprehensive, mathematically rigorous reference source, this book is mostly helpful as a companion to supplement the Statistics 2 course. It is most appropriate for learners who have strong mathematical backgrounds.
This introductory statistics text, which focuses on the social sciences, is appropriate for students who need a more gentle, non-technical approach to probability. It contains many useful examples and practice exercises.
Provides an introduction to bayesian analysis, featuring computational tools and practical applications of Bayesian analysis. The text is useful as supplemental reading and is recommended for advanced learners.
Fundamentals of Probability is more appropriate for those who are interested in a highly conceptual, theorical treatment of probability as it applies to many fields.
Save
While not directly related to the material in Statistics 2 as a probability course, this book can be an excellent supplemental text for learners who are interested in probability as it is applied to econometrics. The text is widely used as a textbook, which guarantees a moderate difficulty curve and ensures its usefulness to the average learner.
This advanced mathematical treatment of probability is useful as a reference for anyone who is interested in the most rigorous, foundational aspects of probability theory. It is not necessary reading for the average Statistics 2 learner.
This massive reference work is highly recommended for researchers and advanced students who are working with multivariate distributions. It is suitable as a reference source for anyone who needs to consult multivariate distributions in Statistics 2.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fbmmc5/statistics
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Effort
12 to 15 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
The London School of Economics and Political Science
Instructor
James Abdey
Language
English
Transcript
English
Good to know
Introduces foundational statistical methods and concepts that are building blocks for other Statistics courses
Develops learners' ability to analyze data, which is applicable across many sectors and disciplines
Covers key statistical ideas from Statistics 1 to strengthen learners' existing understanding
Focuses on probability and distribution theory, providing a comprehensive study of foundational statistics
Emphasizes topics that are particularly relevant to econometrics, finance, and quantitative social science
Taught by instructors from LSE, who are highly respected in the field of statistics
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