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Brian Caffo, PhD
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
Read more
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
Read more
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.
After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Register for this course and see more details by visiting:
OpenCourser.com/course/d0me3e/advanced
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What's inside
Syllabus
Introduction and expected values
In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.
Read more
Syllabus
Introduction and expected values
In this module, we cover the basics of the course as well as the prerequisites. We then cover the basics of expected values for multivariate vectors. We conclude with the moment properties of the ordinary least squares estimates.
Read more
The multivariate normal distribution
In this module, we build up the multivariate and singular normal distribution by starting with iid normals.
Distributional results
In this module, we build the basic distributional results that we see in multivariable regression.
Residuals
In this module we will revisit residuals and consider their distributional results. We also consider the so-called PRESS residuals and show how they can be calculated without re-fitting the model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces advanced concepts in least squares estimation, building a strong foundation for regression modeling
Covers foundational topics in linear algebra and multivariate calculus, making it accessible to learners with a strong mathematical background
Taught by Brian Caffo, PhD, an expert in statistical linear models, providing learners with access to cutting-edge insights
Prerequisites include familiarity with proof-based mathematics, linear algebra, multivariate calculus, and statistics, making it suitable for advanced learners
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Save Advanced Linear Models for Data Science 2: Statistical Linear Models to your list so you can find it easily later:
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Reviews summary
Advanced statistical concepts
Learners say this advanced course provides engaging materials that will likely challenge advanced learners. Advanced concepts are well taught, but could benefit from more real-world examples.
Course material will likely be challenging for some.
"A very challenging and deeply insightful course."
"This course is very powerfull for statistical linear"
Course can help develop valuable data-related skills.
"By taking this course, I improved my Data Management, Statistical Programming, and Statistics skills."
Course could benefit from additional practical applications.
"However, I would've loved further examples that kept bringing things back around to how these things can be used in real world scenarios"
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 Advanced Linear Models for Data Science 2: Statistical Linear Models with these
activities:
Review pre-requisites
Show steps
Ensure that you have a solid foundation in the prerequisites to more easily follow along during the course.
Browse courses on
Linear Algebra
Show steps
-
Review your notes or textbooks on linear algebra.
-
Practice solving problems in multivariate calculus.
-
Revisit concepts in multivariate statistics and probability theory.
Connect with professionals in the field of data science
Show steps
Building connections with professionals can provide valuable insights and career guidance.
Browse courses on
Data Science
Show steps
-
Attend industry events or online meetups.
-
Reach out to professionals on LinkedIn or other platforms.
Follow online tutorials on matrix algebra
Show steps
Matrix algebra is a crucial foundation for linear models. This activity will help you solidify your understanding.
Browse courses on
Matrix Algebra
Show steps
-
Find online tutorials on matrix algebra.
-
Follow the tutorials and complete the practice problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Applied Linear Statistical Models by Michael H. Kutner
Show steps
This book provides a comprehensive and practical introduction to linear models, which will supplement your understanding of the course material.
View
Applied Linear Statistical Models
on Amazon
Show steps
-
Read the chapters relevant to the course content.
-
Work through the examples and exercises provided in the book.
Solve practice problems on linear regression
Show steps
Practice is essential for mastering linear models. This activity will provide you ample opportunities to reinforce your understanding.
Browse courses on
Linear Regression
Show steps
-
Find practice problems on linear regression.
-
Solve the problems using the concepts covered in the course.
-
Compare your solutions with the provided answers.
Participate in a study group and assist other students
Show steps
Engaging with other students can enhance your understanding and solidify your learning.
Browse courses on
Linear Models
Show steps
-
Join a study group for the course.
-
Actively participate in discussions and problem-solving.
-
Assist other students who may need help.
Develop a data visualization for a linear regression model
Show steps
Data visualization is a powerful tool for understanding linear regression models. This activity will allow you to apply your knowledge and gain insights from data.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that is suitable for linear regression.
-
Fit a linear regression model to the data.
-
Create a data visualization to represent the model.
-
Write a brief report that interprets the visualization.
Contribute to an open-source linear regression library
Show steps
Contributing to open-source projects can enhance your technical skills and deepen your understanding of linear models.
Browse courses on
Linear Regression
Show steps
-
Identify an open-source linear regression library that interests you.
-
Read the documentation and contribute to the project.
-
Write a blog post or give a presentation about your experience.
Review pre-requisites
Show steps
Ensure that you have a solid foundation in the prerequisites to more easily follow along during the course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your notes or textbooks on linear algebra.
- Practice solving problems in multivariate calculus.
- Revisit concepts in multivariate statistics and probability theory.
Connect with professionals in the field of data science
Show steps
Building connections with professionals can provide valuable insights and career guidance.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Attend industry events or online meetups.
- Reach out to professionals on LinkedIn or other platforms.
Follow online tutorials on matrix algebra
Show steps
Matrix algebra is a crucial foundation for linear models. This activity will help you solidify your understanding.
Browse courses on
Matrix Algebra
Show steps
Show steps
Show steps
- Find online tutorials on matrix algebra.
- Follow the tutorials and complete the practice problems.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Applied Linear Statistical Models by Michael H. Kutner
Show steps
This book provides a comprehensive and practical introduction to linear models, which will supplement your understanding of the course material.
View
Applied Linear Statistical Models
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to the course content.
- Work through the examples and exercises provided in the book.
Solve practice problems on linear regression
Show steps
Practice is essential for mastering linear models. This activity will provide you ample opportunities to reinforce your understanding.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Find practice problems on linear regression.
- Solve the problems using the concepts covered in the course.
- Compare your solutions with the provided answers.
Participate in a study group and assist other students
Show steps
Engaging with other students can enhance your understanding and solidify your learning.
Browse courses on
Linear Models
Show steps
Show steps
Show steps
- Join a study group for the course.
- Actively participate in discussions and problem-solving.
- Assist other students who may need help.
Develop a data visualization for a linear regression model
Show steps
Data visualization is a powerful tool for understanding linear regression models. This activity will allow you to apply your knowledge and gain insights from data.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that is suitable for linear regression.
- Fit a linear regression model to the data.
- Create a data visualization to represent the model.
- Write a brief report that interprets the visualization.
Contribute to an open-source linear regression library
Show steps
Contributing to open-source projects can enhance your technical skills and deepen your understanding of linear models.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Identify an open-source linear regression library that interests you.
- Read the documentation and contribute to the project.
- Write a blog post or give a presentation about your experience.
Career center
Learners who complete Advanced Linear Models for Data Science 2: Statistical Linear Models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists play a key role in many industries. They use their knowledge of statistics and computer science to extract meaningful insights from data. This course provides a solid foundation in linear algebra, which is essential for building accurate and reliable models. The course also covers the basics of statistical models, which are used to make predictions and inferences from data. This knowledge is essential for Data Scientists who want to be able to use data to solve real-world problems.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. They use their knowledge of statistics and business to make recommendations that can improve the bottom line. This course provides a strong understanding of linear models, which are commonly used to analyze business data. The course also covers the basics of regression analysis, which is a powerful tool for making predictions. This knowledge is essential for Business Analysts who want to be able to make informed decisions based on data.
Statistician
Statistician
Statisticians use data to answer questions and solve problems. They use their knowledge of statistics and mathematics to design and conduct experiments, and to analyze data to draw conclusions. This course provides a strong foundation in linear models, which are commonly used to analyze data. The course also covers the basics of statistical inference, which is used to make conclusions about a population based on a sample. This knowledge is essential for Statisticians who want to be able to answer research questions and solve problems.
Research Scientist
Research Scientist
Research Scientists use data to answer questions and solve problems. They use their knowledge of statistics and science to design and conduct experiments, and to analyze data to draw conclusions. This course provides a strong foundation in linear models, which are commonly used to analyze data in the sciences. The course also covers the basics of statistical inference, which is used to make conclusions about a population based on a sample. This knowledge is essential for Research Scientists who want to be able to answer research questions and solve problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of organizations. They use their knowledge of statistics and mathematics to analyze data and make recommendations that can help organizations improve their operations. This course provides a strong understanding of linear models, which are commonly used to analyze operations research data. The course also covers the basics of optimization, which is used to find the best possible solution to a problem. This knowledge is essential for Operations Research Analysts who want to be able to make recommendations that can help organizations improve their efficiency.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use their knowledge of statistics and mathematics to analyze financial data and make recommendations that can help investors make informed decisions. This course provides a strong understanding of linear models, which are commonly used to analyze financial data. The course also covers the basics of time series analysis, which is used to analyze data over time. This knowledge is essential for Quantitative Analysts who want to be able to make accurate investment recommendations.
Actuary
Actuary
Actuaries use data to assess risk and make financial decisions. They use their knowledge of statistics and mathematics to analyze data and make recommendations that can help businesses and individuals manage risk. This course provides a strong understanding of linear models, which are commonly used to analyze actuarial data. The course also covers the basics of probability and statistics, which are essential for understanding risk. This knowledge is essential for Actuaries who want to be able to make informed decisions about risk.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. They use their knowledge of statistics and marketing to design and conduct surveys, and to analyze data to draw conclusions about consumer preferences. This course provides a strong understanding of linear models, which are commonly used to analyze market research data. The course also covers the basics of multivariate analysis, which is used to analyze data with multiple variables. This knowledge is essential for Market Researchers who want to be able to understand consumer behavior and make recommendations that can help businesses succeed.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They use their knowledge of statistics and finance to analyze financial data and make recommendations that can help investors make informed decisions. This course provides a strong understanding of linear models, which are commonly used to analyze financial data. The course also covers the basics of time series analysis, which is used to analyze data over time. This knowledge is essential for Financial Analysts who want to be able to make accurate investment recommendations.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess risk and make financial decisions. They use their knowledge of statistics and mathematics to analyze data and make recommendations that can help businesses and individuals manage risk. This course provides a strong understanding of linear models, which are commonly used to analyze risk data. The course also covers the basics of probability and statistics, which are essential for understanding risk. This knowledge is essential for Risk Analysts who want to be able to make informed decisions about risk.
Software Engineer
Software Engineer
Software Engineers use data to design and build software. They use their knowledge of statistics and computer science to analyze data and make recommendations that can help improve the quality and performance of software. This course provides a strong foundation in linear models, which are commonly used to analyze software data. The course also covers the basics of statistical software engineering, which is used to build software that can analyze data and make decisions. This knowledge is essential for Software Engineers who want to be able to design and build software that can solve real-world problems.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and improve decision-making. They use their knowledge of statistics and computer science to analyze data and make recommendations that can help businesses and organizations achieve their goals. This course provides a strong foundation in linear models, which are commonly used to analyze data. The course also covers the basics of statistical inference, which is used to make conclusions about a population based on a sample. This knowledge is essential for Data Analysts who want to be able to solve problems and improve decision-making.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use data to build models that can learn from data. They use their knowledge of statistics and computer science to design and build models that can make predictions and decisions. This course provides a strong foundation in linear models, which are commonly used in machine learning. The course also covers the basics of statistical machine learning, which is used to build models that can learn from data. This knowledge is essential for Machine Learning Engineers who want to be able to build models that can solve real-world problems.
For more career information including salaries, visit:
OpenCourser.com/course/d0me3e/advanced
Reading list
We've selected 14 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
Advanced Linear Models for Data Science 2: Statistical Linear Models.
This comprehensive textbook provides a broad overview of statistical theory and methods, covering a wide range of topics, including linear models, making it a valuable resource for students and practitioners.
This advanced textbook provides a comprehensive treatment of linear regression analysis, including topics such as ridge regression, variable selection, and Bayesian methods.
This classic textbook provides a comprehensive treatment of generalized linear models, including logistic regression and other models covered in the course.
This comprehensive textbook provides a thorough introduction to statistical learning methods and their applications in R, making it suitable for both beginners and experienced practitioners.
This advanced textbook provides a comprehensive overview of statistical learning methods, including linear and generalized linear models, and is suitable for advanced students and researchers.
This practical guide to regression modeling provides step-by-step instructions for building and evaluating regression models, including topics such as variable selection, diagnostics, and model validation.
This widely-used textbook provides a comprehensive overview of statistical methods used in psychology, including linear models and other topics covered in the course.
This practical guide to linear models using R software provides step-by-step instructions and examples, making it a valuable resource for students learning linear regression techniques.
This widely-used textbook provides a practical guide to linear regression analysis, including coverage of model selection, diagnostics, and applications.
Save
This practical guide to data mining provides an overview of machine learning techniques, including linear regression and other topics relevant to the course.
This textbook provides a comprehensive overview of statistical methods used in data analysis, including topics such as linear models, logistic regression, and survival analysis.
This textbook provides a comprehensive introduction to multivariate statistical methods, including topics such as principal component analysis, discriminant analysis, and cluster analysis.
This widely-used textbook provides a comprehensive introduction to probability and statistics, including coverage of linear regression and other topics relevant to the course.
Provides a clear and concise introduction to statistical concepts using IBM SPSS Statistics software, making it an excellent resource for students new to statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d0me3e/advanced
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Effort
6 weeks of study, 1-2 hours/week
Level
Advanced
Via
Coursera
Institution
Johns Hopkins University
Instructor
Brian Caffo, PhD
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Advanced Statistics for Data Science. It's comprised of four courses:
- Mathematical Biostatistics Boot Camp 2
- Mathematical Biostatistics Boot Camp 1
- Advanced Linear Models for Data Science 1: Least Squares
- Advanced Linear Models for Data Science 2: Statistical Linear Models (viewing)
Good to know
Introduces advanced concepts in least squares estimation, building a strong foundation for regression modeling
Covers foundational topics in linear algebra and multivariate calculus, making it accessible to learners with a strong mathematical background
Taught by Brian Caffo, PhD, an expert in statistical linear models, providing learners with access to cutting-edge insights
Prerequisites include familiarity with proof-based mathematics, linear algebra, multivariate calculus, and statistics, making it suitable for advanced learners
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