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Kiah Ong
If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.
Read more
If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.
Read more
If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.
This course will focus on getting you acquainted with the generalized linear model (GLM) through the examples of logistic and Poisson regression. You will also see how simple and multiple linear regression relates to GLM using the link function. We will also study a regression technique that is robust to having outliers in the data. Finally, we will learn how to perform model validation involving GLM.
After this course, students will be able to:
- Determine which regression models to use based on the nature of the response variable.
- Use regression technique which is robust to the presence of outliers.
- Perform generalized linear regression using R by identifying the correct link function.
- Interpret and draw conclusions on the regression model.
- Use R to perform statistical inference based on the regression models.
Register for this course and see more details by visiting:
OpenCourser.com/course/w8lfho/variable
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What's inside
Syllabus
Module 1: Logistic Regression
In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. There is a lot to read, watch, and consume in this module so, let’s get started!
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Syllabus
Module 1: Logistic Regression
In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x’s. There is a lot to read, watch, and consume in this module so, let’s get started!
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suitable for those with a technical background, such as mathematics, statistics, computer science, and engineering
Provides examples of logistic and Poisson regression
Covers generalized linear models and their relationship to linear regression
Instructs on the maximum likelihood method for parameter estimation
Implements model validation using R
Might require prerequisite knowledge in some topics
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Reviews summary
Practical regression techniques and model validation
According to learners, this course offers a solid foundation (positive) in generalized linear models (GLM) (neutral), including logistic and Poisson regression (neutral), and introduces robust regression techniques (neutral). Students frequently highlight the course's practical application of R (positive) for model implementation and interpretation, making it highly relevant for data-driven careers (positive). While many appreciate the clear explanations of complex statistical concepts (positive), some note that a strong mathematical or statistical background is essential (warning) to fully grasp the material and keep up with the dense content (warning). The model validation module (positive) is often cited as particularly useful.
Highlights key techniques for handling outliers and validating models.
"The robust regression module was a critical addition, effectively addressing real-world data issues and outliers."
"I learned practical ways to perform model validation, especially for logistic regression, which is invaluable."
"Understanding M-estimators and dealing with outliers significantly improved my data analysis skills."
Builds a solid understanding of GLM and regression principles.
"This course provided a clear understanding of logistic and Poisson regression and their applications."
"I finally understood the link function and how it connects different regression types seamlessly."
"The explanations of maximum likelihood methods were very helpful for grasping the core statistical theory."
Equips learners with essential R skills for statistical modeling.
"I found the R implementation sections incredibly useful for applying the concepts directly."
"The course really helped me perform generalized linear regression using R effectively."
"I appreciated the hands-on practice with R for statistical inference, which is very practical."
Material is extensive and requires dedicated study time.
"There is a lot to read, watch, and consume in each module, so it's important to pace yourself and dedicate time."
"I found the modules very dense, packed with a lot of information that requires careful review."
"The course moves at a rigorous pace, covering many complex topics in a relatively short amount of time."
Demands a solid background in mathematics and statistics.
"Be prepared for a significant amount of math and statistics; it's not for beginners in the field."
"I struggled a bit without a strong prior understanding of advanced statistical theory before starting."
"This course assumes a technical background, so brush up on your linear algebra and probability concepts."
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 Variable Selection, Model Validation, Nonlinear Regression with these
activities:
Review concepts of statistical modeling and generalized linear models
Show steps
Reinforces the foundational concepts you will be using and building upon in this course.
Browse courses on
Statistical Modeling
Show steps
-
Refresh your knowledge of probability and statistics.
-
Review the basics of linear regression.
-
Read through your notes or textbooks on GLMs.
Practice fitting logistic and Poisson regression models
Show steps
Strengthens your understanding of GLM models and provides hands-on experience in fitting them.
Browse courses on
Logistic Regression
Show steps
-
Find a dataset that is suitable for logistic regression.
-
Fit a logistic regression model to the dataset and interpret the results.
-
Repeat steps 1-2 for Poisson regression.
Show all two activities
Review concepts of statistical modeling and generalized linear models
Show steps
Reinforces the foundational concepts you will be using and building upon in this course.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Refresh your knowledge of probability and statistics.
- Review the basics of linear regression.
- Read through your notes or textbooks on GLMs.
Practice fitting logistic and Poisson regression models
Show steps
Strengthens your understanding of GLM models and provides hands-on experience in fitting them.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a dataset that is suitable for logistic regression.
- Fit a logistic regression model to the dataset and interpret the results.
- Repeat steps 1-2 for Poisson regression.
Show all two activities
Career center
Learners who complete Variable Selection, Model Validation, Nonlinear Regression will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians design, conduct, analyze, and interpret statistical studies. This course can help you gain the skills necessary to design and conduct statistical studies, analyze data, and interpret results. This course can also help you build a foundation in generalized linear models, which are commonly used in statistical studies.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course can help you gain the skills necessary to collect, clean, and analyze data, and use generalized linear models to make predictions and draw conclusions.
Financial Analyst
Financial Analyst
Financial analysts make investment recommendations and provide advice to clients. This course can help you gain the skills necessary to analyze financial data and make informed investment decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and develop insurance products. This course can help you gain the skills necessary to assess risk and develop insurance products, particularly in the context of non-life insurance.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical techniques to solve business problems. This course can help you gain the skills necessary to solve business problems, particularly in the context of supply chain management and logistics.
Market Research Analyst
Market Research Analyst
Market research analysts collect and analyze data to help businesses understand their customers and make informed marketing decisions. This course can help you gain the skills necessary to collect and analyze data, and use generalized linear models to make predictions and draw conclusions.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and develop strategies to prevent and control it. This course can help you gain the skills necessary to investigate the causes of disease and develop strategies to prevent and control it, particularly in the context of non-communicable diseases.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course can help you gain the skills necessary to apply statistical methods to solve problems in biology and medicine, particularly in the context of clinical trials.
Software Engineer
Software Engineer
Software engineers design, develop, and test computer software. This course may be helpful for software engineers who want to gain a better understanding of statistical methods and how they can be used to improve software design and development.
Data Scientist
Data Scientist
Data scientists use data to solve business problems. This course may be helpful for data scientists who want to gain a better understanding of statistical methods and how they can be used to solve business problems.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to develop financial models. This course may be helpful for quantitative analysts who want to gain a better understanding of statistical methods and how they can be used to develop financial models.
Business Analyst
Business Analyst
Business analysts help businesses improve their operations and make better decisions. This course may be helpful for business analysts who want to gain a better understanding of statistical methods and how they can be used to improve business operations and make better decisions.
Risk Manager
Risk Manager
Risk managers identify and assess risks, and develop strategies to mitigate them. This course may be helpful for risk managers who want to gain a better understanding of statistical methods and how they can be used to identify, assess, and mitigate risks.
Economist
Economist
Economists study how people make decisions and how those decisions affect the economy. This course may be helpful for economists who want to gain a better understanding of statistical methods and how they can be used to study economic data and make economic predictions.
Teacher
Teacher
Teachers help students learn and grow. This course may be helpful for teachers who want to gain a better understanding of statistical methods and how they can be used to evaluate teaching methods and student learning.
For more career information including salaries, visit:
OpenCourser.com/course/w8lfho/variable
Reading list
We've selected seven 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
Variable Selection, Model Validation, Nonlinear Regression.
Provides a comprehensive treatment of generalized linear models (GLMs), which are a family of statistical models that include logistic regression and Poisson regression. It valuable resource for anyone who wants to learn more about GLMs or use them in their own research.
Provides a comprehensive introduction to statistical learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about statistical learning or use it in their own research.
Provides a comprehensive treatment of pattern recognition and machine learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about pattern recognition or machine learning or use it in their own research.
Save
Provides a comprehensive treatment of statistical methods for machine learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about statistical methods for machine learning or use them in their own research.
Provides a comprehensive treatment of deep learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about deep learning or use it in their own research.
Provides a more advanced treatment of statistical learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about statistical learning or use it in their own research.
Provides a comprehensive introduction to machine learning, including a chapter on GLMs. It valuable resource for anyone who wants to learn more about machine learning or use it in their own research.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w8lfho/variable
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