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Najib Mozahem
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed.
In the second part of the course, students learn how to apply what they have learned using Stata. In this part, students will walk through a large project in order to fit Poisson, negative binomial, and zero-inflated models. The tools used to compare these models are also introduced.
Register for this course and see more details by visiting:
OpenCourser.com/course/qnaell/modeling
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What's inside
Learning objectives
- Understand count tables
- Calculate incidence-rate ratios
- Understand what count models are
- Identify when to use count models
- Poisson regression
- Negative binomial regression
- Truncated models
- Zero-inflated models
- Predict expected number of outcomes
- Apply count models using stata
- Compare different models
- Visualise the results
- Show more
- Show less
- Understand count tables
- Calculate incidence-rate ratios
- Understand what count models are
- Identify when to use count models
- Poisson regression
- Negative binomial regression
- Truncated models
- Zero-inflated models
- Predict expected number of outcomes
- Apply count models using stata
- Compare different models
- Visualise the results
- Show more
- Show less
Syllabus
Count tables
Introduction
Risk
Inceidence-rate ratio
Read more
Syllabus
Count tables
Introduction
Risk
Inceidence-rate ratio
Read more
Two-by-three tables
Poisson regression
Single independent variable
Examples
Binary variables
Multiple independent variables
Categorical variables
Exposure
Other count models
Negative binomial regression
Truncated models
Zero-inflated models
Comparison of models
Prediction
Predicting the number of events
Predicting probabilities of certain counts
Application: Fitting the model
Introduction to the dataset
Continuous variables
Multivariate analysis
Application: Model Comparison and Prediction
Comparing count models
Model interpretation: predicted number of events
Model interpretation: predicted probabilities of different outcomes
Visualizing the model: predicted number of events
Visualizing the model: predicted probabilities of different outcomes
Conclusion
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Goes into depth and provides a deep understanding of count models for analyzing count data
Empowers learners to apply these models using Stata, a leading statistical software
Suitable for beginners who seek to grasp the fundamentals of count models, as it presents the theory intuitively with minimal math
Covers a comprehensive range of count models, including Poisson, negative binomial, truncated, and zero-inflated models
Provides hands-on practice through a large project, allowing learners to apply their knowledge and gain practical experience
Led by experienced instructor Najib Mozahem, recognized for his expertise in count models
Save this course
Save Modeling Count Data using Stata 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 Modeling Count Data using Stata with these
activities:
Refine statistics knowledge
Show steps
Review statistical concepts and techniques for analyzing data
Browse courses on
Hypothesis Testing
Show steps
-
Review basic statistical concepts (mean, median, mode, standard deviation)
-
Practice calculating probabilities using probability distributions
-
Test hypotheses using statistical tests
Organize and summarize course content
Show steps
Improve retention by creating a comprehensive study resource
Show steps
-
Review lecture notes, slides, and textbook materials
-
Identify and extract key concepts and ideas
-
Create outlines, summaries, or mind maps to organize the information
-
Use different colors, fonts, or visual aids to enhance memorability
Create a dataset simulating count data
Show steps
Build a strong foundation by understanding the generation of count data
Show steps
-
Learn about different methods for simulating count data, such as the Poisson and negative binomial distributions
-
Write code to simulate a dataset using the chosen method
-
Explore the characteristics of the simulated data and compare it to real-world count data
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer discussions on Stata
Show steps
Improve Stata proficiency and gain insights from peer discussions
Browse courses on
Stata
Show steps
-
Join online forums or study groups dedicated to Stata users
-
Engage in discussions and ask questions related to the course content
-
Share knowledge and help other peers with their Stata queries
Follow online tutorials on Poisson regression
Show steps
Enhance understanding of Poisson regression through guided video instructions
Browse courses on
Poisson Regression
Show steps
-
Search for online tutorials on Poisson regression
-
Select tutorials that align with the course content
-
Follow the tutorials and complete the exercises
Solve practice problems on count models
Show steps
Reinforce understanding by applying count models to practical scenarios
Browse courses on
Poisson Regression
Show steps
-
Gather practice problems from online resources or textbooks
-
Solve the problems using the concepts learned in the course
-
Compare solutions with provided answers or consult the instructor for feedback
Develop a research proposal using count models
Show steps
Apply course concepts by designing a research study using count models
Browse courses on
Research Proposal
Show steps
-
Define a research question that can be addressed using count models
-
Conduct a literature review to identify relevant studies and theoretical frameworks
-
Develop a research design and methodology outlining the use of count models
-
Write the research proposal, including sections on the introduction, literature review, methodology, and expected outcomes
Volunteer at a research center or statistical consulting firm
Show steps
Gain practical experience and contribute to research projects using count models
Browse courses on
Research
Show steps
-
Identify research centers or statistical consulting firms that work with count models
-
Contact the organizations and inquire about volunteer opportunities
-
Assist with data collection, analysis, or other tasks related to count models
Refine statistics knowledge
Show steps
Review statistical concepts and techniques for analyzing data
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Review basic statistical concepts (mean, median, mode, standard deviation)
- Practice calculating probabilities using probability distributions
- Test hypotheses using statistical tests
Organize and summarize course content
Show steps
Improve retention by creating a comprehensive study resource
Show steps
Show steps
Show steps
- Review lecture notes, slides, and textbook materials
- Identify and extract key concepts and ideas
- Create outlines, summaries, or mind maps to organize the information
- Use different colors, fonts, or visual aids to enhance memorability
Create a dataset simulating count data
Show steps
Build a strong foundation by understanding the generation of count data
Show steps
Show steps
Show steps
- Learn about different methods for simulating count data, such as the Poisson and negative binomial distributions
- Write code to simulate a dataset using the chosen method
- Explore the characteristics of the simulated data and compare it to real-world count data
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer discussions on Stata
Show steps
Improve Stata proficiency and gain insights from peer discussions
Browse courses on
Stata
Show steps
Show steps
Show steps
- Join online forums or study groups dedicated to Stata users
- Engage in discussions and ask questions related to the course content
- Share knowledge and help other peers with their Stata queries
Follow online tutorials on Poisson regression
Show steps
Enhance understanding of Poisson regression through guided video instructions
Browse courses on
Poisson Regression
Show steps
Show steps
Show steps
- Search for online tutorials on Poisson regression
- Select tutorials that align with the course content
- Follow the tutorials and complete the exercises
Solve practice problems on count models
Show steps
Reinforce understanding by applying count models to practical scenarios
Browse courses on
Poisson Regression
Show steps
Show steps
Show steps
- Gather practice problems from online resources or textbooks
- Solve the problems using the concepts learned in the course
- Compare solutions with provided answers or consult the instructor for feedback
Develop a research proposal using count models
Show steps
Apply course concepts by designing a research study using count models
Browse courses on
Research Proposal
Show steps
Show steps
Show steps
- Define a research question that can be addressed using count models
- Conduct a literature review to identify relevant studies and theoretical frameworks
- Develop a research design and methodology outlining the use of count models
- Write the research proposal, including sections on the introduction, literature review, methodology, and expected outcomes
Volunteer at a research center or statistical consulting firm
Show steps
Gain practical experience and contribute to research projects using count models
Browse courses on
Research
Show steps
Show steps
Show steps
- Identify research centers or statistical consulting firms that work with count models
- Contact the organizations and inquire about volunteer opportunities
- Assist with data collection, analysis, or other tasks related to count models
Career center
Learners who complete Modeling Count Data using Stata will develop knowledge and skills
that may be useful to these careers:
Actuary
Actuary
Actuaries use statistical modeling to assess risk, and the key component of this course is the modeling of count data. Count data is data that can only take on whole number values, such as the number of claims filed with an insurance company. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for actuaries. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that actuaries commonly use.
Statistician
Statistician
Statisticians use statistical modeling to analyze data and draw conclusions from it. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for statisticians. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that statisticians commonly use.
Data Scientist
Data Scientist
Data scientists apply statistical modeling and other techniques to solve business problems. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for data scientists. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that data scientists commonly use.
Market Research Analyst
Market Research Analyst
Market research analysts use statistical modeling to analyze consumer behavior. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for market research analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that market research analysts commonly use.
Financial Analyst
Financial Analyst
Financial analysts use statistical modeling to analyze financial data. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for financial analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that financial analysts commonly use.
Epidemiologist
Epidemiologist
Epidemiologists use statistical modeling to study the spread of disease. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for epidemiologists. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that epidemiologists commonly use.
Public Health Analyst
Public Health Analyst
Public health analysts use statistical modeling to analyze public health data. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for public health analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that public health analysts commonly use.
Risk Manager
Risk Manager
Risk managers use statistical modeling to assess risk. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a critical skill for risk managers. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that risk managers commonly use.
Data Analyst
Data Analyst
Data analysts use statistical modeling to analyze data and draw conclusions from it. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a useful skill for data analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that data analysts may use.
Market Researcher
Market Researcher
Market researchers use statistical modeling to analyze consumer behavior. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a useful skill for market researchers. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that market researchers may use.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use statistical modeling to solve business problems. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a useful skill for operations research analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that operations research analysts may use.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use statistical modeling to analyze financial data. This course will provide you with the skills you need to build statistical models to predict the number of events that will occur in the future, which is a useful skill for quantitative analysts. Additionally, the course covers several different count models, including Poisson regression, negative binomial regression, truncated models, and zero-inflated models, which are all models that quantitative analysts may use.
Software Engineer
Software Engineer
Software engineers use statistical modeling to design and develop software. This course may provide you with some of the skills you need to build statistical models, but it is not a substitute for a formal education in software engineering. However, the course may be useful for software engineers who want to learn more about statistical modeling.
Business Analyst
Business Analyst
Business analysts use statistical modeling to analyze business data. This course may provide you with some of the skills you need to build statistical models, but it is not a substitute for a formal education in business analysis. However, the course may be useful for business analysts who want to learn more about statistical modeling.
Financial Advisor
Financial Advisor
Financial advisors use statistical modeling to analyze financial data. This course may provide you with some of the skills you need to build statistical models, but it is not a substitute for a formal education in financial advising. However, the course may be useful for financial advisors who want to learn more about statistical modeling.
For more career information including salaries, visit:
OpenCourser.com/course/qnaell/modeling
Reading list
We've selected nine 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
Modeling Count Data using Stata.
Provides a practical guide to fitting count data models in Stata. It covers a wide range of models, including Poisson regression, negative binomial regression, and zero-inflated models. It valuable resource for students and researchers who want to use Stata to analyze count data.
Provides a comprehensive overview of generalized linear models, including count data models. It valuable resource for students and researchers who want to learn more about the theory and application of generalized linear models.
Provides a comprehensive overview of statistical methods for count data. It valuable resource for students and researchers who want to learn more about the theory and application of statistical methods for count data.
Provides a comprehensive overview of Poisson regression for count data. It valuable resource for students and researchers who want to learn more about the theory and application of Poisson regression for count data.
Provides a comprehensive overview of negative binomial regression. It valuable resource for students and researchers who want to learn more about the theory and application of negative binomial regression.
Provides a comprehensive overview of count data regression analysis. It valuable resource for students and researchers who want to learn more about the theory and application of count data regression analysis.
Provides a comprehensive overview of probability and statistics. It valuable resource for students and researchers who want to learn more about the theory and application of probability and statistics.
Provides a comprehensive overview of statistical inference. It valuable resource for students and researchers who want to learn more about the theory and application of statistical inference.
Provides a comprehensive overview of statistics for social scientists. It valuable resource for students and researchers who want to learn more about the theory and application of statistics for social scientists.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qnaell/modeling
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Effort
3 total hours
Via
Udemy
Instructor
Najib Mozahem
Language
English
Good to know
Goes into depth and provides a deep understanding of count models for analyzing count data
Empowers learners to apply these models using Stata, a leading statistical software
Suitable for beginners who seek to grasp the fundamentals of count models, as it presents the theory intuitively with minimal math
Covers a comprehensive range of count models, including Poisson, negative binomial, truncated, and zero-inflated models
Provides hands-on practice through a large project, allowing learners to apply their knowledge and gain practical experience
Led by experienced instructor Najib Mozahem, recognized for his expertise in count models
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