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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
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!
Read more
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
Module 2: Poisson Regression and Generalized Linear Model
In this module, you will learn the difference between Poisson regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, use R to compute the estimators of a Poisson regression model and the generalized linear model, and the similarities between the linear, logistic, and Poisson regressions. There is a lot to read, watch, and consume in this module so, let’s get started!
Module 3: Robust Regression and Model Validation
In this module, you will learn how to modify the ordinary least squares method to make the regression model more robust to the effect of outliers and use R to compute the robust regression parameters using different M-estimators and perform model validations involving logistic regression. There is a lot to read, watch, and consume in this module so, let’s get started!
Summative Course Assessment
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Variable Selection, Model Validation, Nonlinear Regression 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 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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Level
Intermediate
Via
Coursera
Institution
Illinois Tech
Instructor
Kiah Ong
Language
English
Good to know
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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