Generalized Linear Models
Generalized Linear Models (GLMs) are a type of statistical model used to analyze data. They are based on a canonical link function and use likelihood, score, and Fisher Information to estimate parameters. An iteratively re-weighted least squares method is used for a general link function. GLMs are used to model a wide range of data types, including binary, count, and continuous data. ▼
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Course Feature
Cost:
Free
Provider:
Youtube
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
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Updated in [February 21st, 2023]
What does this course tell?
(Please note that the following overview content is from the original platform)
Generalized Linear Models: Background.
Generalized Linear Models: Canonical Link Function.
Generalized Linear Models: Likelihood, Score, and Fisher Information.
GLM: Iteratively Re-weighted Least Squares for a General Link Function.
Generalized Linear Models: Probit Regression (part 1).
Generalized Linear Models: Probit Regression (part 2).
Generalized Linear Models: Logistic "Logit" Regression (part 1).
Generalized Linear Models: Logistic "Logit" Regression (part 2).
Generalized Linear Models: Logistic "Logit" Regression (part 2).
Generalized Linear Models: Complementary Log Log Regression (part 1).
Generalized Linear Models: Complementary Log Log Regression (part 2).
Generalized Linear Models: Complementary Log Log Regression (part 2).
Generalized Linear Models: Poisson Regression with Canonical Link (part 1).
Generalized Linear Models: Poisson Regression with Canonical Link (part 2).
Ordinal Logistic Regression (Proportional Odds Model).
Multinomial Logistic Regression.
What can you get from this course?
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
This course will provide students with the skills and knowledge to understand and apply Generalized Linear Models. Students will learn about the background of GLMs, the canonical link function, likelihood, score, and Fisher information, and how to use Iteratively Re-weighted Least Squares for a General Link Function. Additionally, students will learn about Probit Regression, Logistic "Logit" Regression, Complementary Log Log Regression, and Poisson Regression with Canonical Link. Finally, students will learn about Ordinal Logistic Regression (Proportional Odds Model) and Multinomial Logistic Regression.
How does this course contribute to professional growth?
This course provides a comprehensive overview of Generalized Linear Models (GLMs) and their applications. It covers topics such as the background of GLMs, canonical link functions, likelihood, score, and Fisher information, iteratively re-weighted least squares for a general link function, probit regression, logistic "logit" regression, complementary log log regression, and Poisson regression with canonical link. Additionally, the course covers ordinal logistic regression and multinomial logistic regression. By taking this course, professionals can gain a better understanding of GLMs and their applications, which can help them to make more informed decisions and improve their professional growth.
Is this course suitable for preparing further education?
This course is suitable for preparing further education, as it provides a comprehensive overview of GLMs and their applications.
Course Provider
Provider Youtube's Stats at AZClass
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