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Elena Moltchanova
Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate.
Read more
Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate.
Read more
Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate.
This course is directed at people who are already familiar with basic linear regression and fundamentals of statistical inference. It extends the knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up. While very practice oriented, it aims to give the students the understanding of why the method works (theory), how to implement it (programming using R) and when to apply it (and where to look if the particular method is not applicable in the specific situation).
What you'll learn
- Exploratory data analysis and data visualisation using R.
- Multivariate analysis using Generalised Linear Models (GLMs):
- Binary response (logistic regression) GLM
- Poisson counts GLM
- Nominal categorical response (multinomial logistic GLM)
- Ordinal categorical response (ordinal logistic GLM)
- Mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Model selection.
- Basics of power analysis (sample size evaluation) and some thoughts on experimental design and missing data.
Register for this course and see more details by visiting:
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What's inside
Learning objectives
- Exploratory data analysis and data visualisation using r.
- Multivariate analysis using generalised linear models (glms):
- Binary response (logistic regression) glm
- Poisson counts glm
- Nominal categorical response (multinomial logistic glm)
- Ordinal categorical response (ordinal logistic glm)
- Mixed effects linear regression models. structure, assumptions, diagnostics and interpretation. model selection.
- Basics of power analysis (sample size evaluation) and some thoughts on experimental design and missing data.
- Exploratory data analysis and data visualisation using r.
- Multivariate analysis using generalised linear models (glms):
- Binary response (logistic regression) glm
- Poisson counts glm
- Nominal categorical response (multinomial logistic glm)
- Ordinal categorical response (ordinal logistic glm)
- Mixed effects linear regression models. structure, assumptions, diagnostics and interpretation. model selection.
- Basics of power analysis (sample size evaluation) and some thoughts on experimental design and missing data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Expands upon fundamentals, covering a broader range of statistical techniques and concepts
Requires learners to come in with knowledge of basic linear regression and statistical inference
Led by an instructor with experience in teaching advanced statistical methods
Teaches tools and techniques that are widely used in industry and research
Includes hands-on labs and interactive materials to reinforce learning
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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 Advanced Statistical Inference and Modelling Using R with these
activities:
Create a Comprehensive Course Summary
Show steps
Summarizes key concepts, facilitates retention, and provides a valuable review resource for exam preparation.
Show steps
-
Organize notes from each lecture
-
Create outlines or mind maps to condense information
-
Review assignments, quizzes, and exams for additional insights
-
Consolidate materials into a comprehensive summary
Review Basic Probability and Statistics
Show steps
Brushing up on concepts from elementary probability and statistics will provide a strong foundation for understanding the course material.
Browse courses on
Probability
Show steps
-
Review probability distributions, such as binomial, normal, and Poisson.
-
Practice calculating probabilities and expected values.
-
Review hypothesis testing and confidence intervals.
-
Review linear regression and its assumptions.
Participate in Study Groups with Classmates
Show steps
Engaging in peer learning through study groups will foster collaboration and improve understanding of course material.
Show steps
-
Form study groups with classmates.
-
Regularly meet to discuss course concepts, work on assignments together, and clarify doubts.
-
Present and explain solutions to problems to each other.
-
Provide constructive feedback and support to group members.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
General Linear Regression Formula Practice
Show steps
Provides additional experience with the formulas and methods underlying generalized linear models.
Browse courses on
Linear Regression
Show steps
-
Solve problems involving linear regression
-
Review the key assumptions of linear regression
-
Apply linear regression to real-world data
Follow Tutorials on GLMs and Mixed Effects Models
Show steps
Engaging in guided tutorials will provide hands-on experience in implementing the statistical techniques covered in the course.
Browse courses on
Mixed Effects Models
Show steps
-
Find tutorials on GLMs, such as logistic and Poisson regression.
-
Follow the tutorials step-by-step, practicing with sample data.
-
Explore tutorials on mixed effects models, focusing on their structure and assumptions.
-
Apply the techniques learned in the tutorials to analyze real-world datasets.
Solve Practice Problems on Statistical Inference
Show steps
Solving practice problems will reinforce the concepts of statistical inference and improve students' problem-solving skills.
Browse courses on
Statistical Inference
Show steps
-
Find practice problems related to hypothesis testing, confidence intervals, and power analysis.
-
Attempt to solve the problems independently.
-
Check solutions and identify areas for improvement.
-
Repeat the process to strengthen understanding.
Walkthrough of Advanced Power Analysis
Show steps
Deepens the understanding of power analysis and helps to develop proficiency in conducting it.
Browse courses on
Power Analysis
Show steps
-
Follow a guided tutorial on advanced power analysis
-
Practice applying the techniques
-
Discuss the results with instructors or peers
Resource Compilation: Statistical Tools and Techniques
Show steps
Provides a valuable reference for future use and deepens the understanding of statistical tools and techniques.
Show steps
-
Identify and collect relevant articles, tutorials, and code snippets
-
Organize the resources into meaningful categories
-
Annotate the resources with brief summaries and explanations
Develop a Data Analysis Plan for a Research Project
Show steps
Creating a data analysis plan will help students apply the statistical knowledge and techniques from the course to a practical research scenario.
Browse courses on
Data Analysis
Show steps
-
Define the research question and identify the relevant variables.
-
Choose appropriate GLMs or mixed effects models based on the research question and data structure.
-
Develop a detailed plan for data collection, data cleaning, and data analysis.
-
Present the data analysis plan in a clear and organized manner.
Review: Generalized Linear Mixed Models by McCullagh and Nelder
Show steps
Reviewing this seminal work on GLMs and mixed effects models will provide a deeper understanding of the theoretical foundations and applications of these techniques.
View
Generalized Linear Models (Chapman & Hall/CRC...
on Amazon
Show steps
-
Read and understand the key concepts presented in the book.
-
Pay attention to the mathematical derivations and examples provided.
-
Link the concepts to the material covered in the course.
Modeling a Real-World Dataset
Show steps
Provides hands-on experience and deepens the understanding of applying statistical models to solve real-world problems.
Show steps
-
Identify a dataset related to the course topics
-
Explore and clean the data
-
Develop and fit statistical models to the data
-
Evaluate the models and interpret the results
Start a Personal Data Analysis Project
Show steps
Embarking on a personal data analysis project will allow students to apply the techniques learned in the course to a real-world problem.
Browse courses on
Data Analysis
Show steps
-
Identify a topic of interest and formulate a research question.
-
Collect or gather relevant data.
-
Clean and prepare the data for analysis.
-
Apply GLMs or mixed effects models to analyze the data and draw conclusions.
-
Write a report summarizing the project findings.
Create a Comprehensive Course Summary
Show steps
Summarizes key concepts, facilitates retention, and provides a valuable review resource for exam preparation.
Show steps
Show steps
Show steps
- Organize notes from each lecture
- Create outlines or mind maps to condense information
- Review assignments, quizzes, and exams for additional insights
- Consolidate materials into a comprehensive summary
Review Basic Probability and Statistics
Show steps
Brushing up on concepts from elementary probability and statistics will provide a strong foundation for understanding the course material.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distributions, such as binomial, normal, and Poisson.
- Practice calculating probabilities and expected values.
- Review hypothesis testing and confidence intervals.
- Review linear regression and its assumptions.
Participate in Study Groups with Classmates
Show steps
Engaging in peer learning through study groups will foster collaboration and improve understanding of course material.
Show steps
Show steps
Show steps
- Form study groups with classmates.
- Regularly meet to discuss course concepts, work on assignments together, and clarify doubts.
- Present and explain solutions to problems to each other.
- Provide constructive feedback and support to group members.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
General Linear Regression Formula Practice
Show steps
Provides additional experience with the formulas and methods underlying generalized linear models.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Solve problems involving linear regression
- Review the key assumptions of linear regression
- Apply linear regression to real-world data
Follow Tutorials on GLMs and Mixed Effects Models
Show steps
Engaging in guided tutorials will provide hands-on experience in implementing the statistical techniques covered in the course.
Browse courses on
Mixed Effects Models
Show steps
Show steps
Show steps
- Find tutorials on GLMs, such as logistic and Poisson regression.
- Follow the tutorials step-by-step, practicing with sample data.
- Explore tutorials on mixed effects models, focusing on their structure and assumptions.
- Apply the techniques learned in the tutorials to analyze real-world datasets.
Solve Practice Problems on Statistical Inference
Show steps
Solving practice problems will reinforce the concepts of statistical inference and improve students' problem-solving skills.
Browse courses on
Statistical Inference
Show steps
Show steps
Show steps
- Find practice problems related to hypothesis testing, confidence intervals, and power analysis.
- Attempt to solve the problems independently.
- Check solutions and identify areas for improvement.
- Repeat the process to strengthen understanding.
Walkthrough of Advanced Power Analysis
Show steps
Deepens the understanding of power analysis and helps to develop proficiency in conducting it.
Browse courses on
Power Analysis
Show steps
Show steps
Show steps
- Follow a guided tutorial on advanced power analysis
- Practice applying the techniques
- Discuss the results with instructors or peers
Resource Compilation: Statistical Tools and Techniques
Show steps
Provides a valuable reference for future use and deepens the understanding of statistical tools and techniques.
Show steps
Show steps
Show steps
- Identify and collect relevant articles, tutorials, and code snippets
- Organize the resources into meaningful categories
- Annotate the resources with brief summaries and explanations
Develop a Data Analysis Plan for a Research Project
Show steps
Creating a data analysis plan will help students apply the statistical knowledge and techniques from the course to a practical research scenario.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the research question and identify the relevant variables.
- Choose appropriate GLMs or mixed effects models based on the research question and data structure.
- Develop a detailed plan for data collection, data cleaning, and data analysis.
- Present the data analysis plan in a clear and organized manner.
Review: Generalized Linear Mixed Models by McCullagh and Nelder
Show steps
Reviewing this seminal work on GLMs and mixed effects models will provide a deeper understanding of the theoretical foundations and applications of these techniques.
View
Generalized Linear Models (Chapman & Hall/CRC...
on Amazon
Show steps
Show steps
Show steps
- Read and understand the key concepts presented in the book.
- Pay attention to the mathematical derivations and examples provided.
- Link the concepts to the material covered in the course.
Modeling a Real-World Dataset
Show steps
Provides hands-on experience and deepens the understanding of applying statistical models to solve real-world problems.
Show steps
Show steps
Show steps
- Identify a dataset related to the course topics
- Explore and clean the data
- Develop and fit statistical models to the data
- Evaluate the models and interpret the results
Start a Personal Data Analysis Project
Show steps
Embarking on a personal data analysis project will allow students to apply the techniques learned in the course to a real-world problem.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a topic of interest and formulate a research question.
- Collect or gather relevant data.
- Clean and prepare the data for analysis.
- Apply GLMs or mixed effects models to analyze the data and draw conclusions.
- Write a report summarizing the project findings.
Career center
Learners who complete Advanced Statistical Inference and Modelling Using R will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians use statistical theories and methods to collect, analyze, interpret, and present data, helping make informed decisions. This course will provide an introduction to advanced statistical concepts and techniques such as generalized linear models (GLMs), mixed effects models, and power analysis, all valuable tools for the modern Statistician. Mastering these techniques will enhance the ability to analyze complex data, make valid inferences, and provide insightful recommendations.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health and disease in populations. The statistical modeling techniques covered in this course are essential for Epidemiologists to analyze epidemiological data. GLMs, including logistic regression for binary outcomes and Poisson regression for count outcomes, enable them to assess risk factors, study disease outbreaks, and evaluate the effectiveness of public health interventions.
Public Health Analyst
Public Health Analyst
Public Health Analysts use data to assess and improve the health of communities. The statistical modeling techniques covered in this course, such as GLMs for binary and count outcomes and mixed effects models for hierarchical data, are crucial for analyzing public health data and understanding health trends. By taking this course, aspiring Public Health Analysts will gain the skills to evaluate the effectiveness of public health interventions, identify risk factors for diseases, and develop data-driven policies to improve population health.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in the medical field. This course provides a solid foundation in advanced statistical modeling techniques crucial for Biostatisticians to analyze complex medical data. Specifically, the coverage of GLMs for binary and count outcomes, mixed effects models for hierarchical data, and power analysis will enhance their ability to design and analyze clinical trials, assess the effectiveness of treatments, and contribute to medical research.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data and test economic theories. This course provides a strong foundation in advanced statistical modeling, including GLMs and mixed effects models, which are essential for Econometricians to analyze economic relationships. The course will equip them with the skills to build sophisticated models, handle hierarchical data structures, and draw meaningful conclusions from economic data.
Data Analyst
Data Analyst
A Data Analyst uses modeling and statistics to analyze data and interpret relationships between variables. Those who wish to become Data Analysts may take this course to learn regression analysis, GLMs, and visualization in R. This course covers the applications of these models in the analysis of binary, count, and categorical response variables. The mixed effects regression taught in this course will especially help Data Analysts build models for hierarchical data structures.
Data Scientist
Data Scientist
Data Scientists leverage data to extract valuable insights and make informed decisions. This course delves into advanced statistical modeling techniques, including GLMs and mixed effects models, essential for Data Scientists to analyze complex and diverse datasets. The knowledge and skills gained from this course will enable them to build more sophisticated models, handle hierarchical data structures, and make reliable predictions.
Computational Biologist
Computational Biologist
Computational Biologists use computational tools and techniques to solve problems in biology and medicine. The statistical modeling concepts and methods taught in this course, such as GLMs for binary and count outcomes, are essential for analyzing biological data, identifying patterns, and making predictions. This course will provide a strong foundation for Computational Biologists to contribute to cutting-edge research in personalized medicine, drug discovery, and bioinformatics.
Quantitative Analyst (Quant)
Quantitative Analyst (Quant)
Quantitative Analysts apply mathematical and statistical techniques to solve business problems and make data-driven decisions. The advanced statistical inference and modeling taught in this course are valuable skills for a Quant. Understanding GLMs, mixed effects models, and power analysis will equip individuals seeking a career in quantitative analysis to handle complex financial data and make more accurate predictions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models to solve complex problems. This course provides a strong foundation in statistical modeling essential for a successful Machine Learning Engineer. The emphasis on GLMs, mixed effects models, and power analysis will enable them to develop more robust and accurate machine learning models, especially for tasks involving binary classification, count data analysis, or hierarchical data structures.
Actuary
Actuary
Actuaries assess and manage financial risks. This course provides foundational knowledge in statistical modeling, particularly GLMs and mixed effects models, which are widely used in actuarial practice. By understanding these techniques, aspiring Actuaries can more accurately model insurance premiums, predict financial outcomes, and make informed decisions regarding risk management.
Business Analyst
Business Analyst
Business Analysts use data to analyze and improve business performance. This course provides a solid grounding in statistical modeling, including GLMs and mixed effects models, which are used in business analytics. By mastering these techniques, aspiring Business Analysts can develop more robust analytical models, gain insights from complex data, and make data-driven recommendations for improving business outcomes.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve efficiency and decision-making in organizations. This course provides a solid grounding in statistical modeling, including GLMs and mixed effects models, which are widely used in operations research. By mastering these techniques, aspiring Operations Research Analysts can develop more effective optimization models, analyze complex data, and make better recommendations for improving operational processes.
Survey Researcher
Survey Researcher
Survey Researchers design, conduct, and analyze surveys to collect data on a population. This course provides a strong foundation in statistical modeling, particularly GLMs, which are widely used in survey research. By understanding these techniques, Survey Researchers can more effectively analyze survey data, identify patterns and trends, and make accurate inferences about the population.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret market data to understand consumer behavior and trends. This course offers valuable statistical modeling techniques that Market Researchers need to conduct robust market research. The focus on GLMs, including logistic regression for binary outcomes and multinomial logistic regression for categorical outcomes, will allow them to analyze survey data and gain insights into consumer preferences, market Segmentation, and campaign effectiveness.
For more career information including salaries, visit:
OpenCourser.com/course/zgbzk5/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 Statistical Inference and Modelling Using R.
Provides a comprehensive introduction to generalized linear models (GLMs) and their applications in R. It covers the theory and implementation of GLMs, as well as case studies and examples.
Provides a comprehensive introduction to statistical learning methods and their applications in R. It covers a wide range of topics, including linear regression, logistic regression, and mixed effects models.
Provides a comprehensive introduction to mixed effects models and their applications in ecology. It covers the theory and implementation of mixed effects models, as well as case studies and examples.
Provides a comprehensive introduction to generalized linear mixed models (GLMM). It covers the theory and implementation of GLMM, as well as case studies and examples.
Provides a comprehensive introduction to regression and multilevel/hierarchical models. It covers the theory and implementation of these models, as well as case studies and examples.
Provides a comprehensive introduction to logistic regression and its applications. It covers the theory and implementation of logistic regression, as well as case studies and examples.
Provides a comprehensive introduction to Bayesian data analysis. It covers the theory and implementation of Bayesian models, as well as case studies and examples.
Save
Provides a comprehensive introduction to the R programming language and its applications in statistics. It covers a wide range of topics, including data manipulation, data visualization, and statistical modeling.
Save
Provides a comprehensive introduction to causal inference. It covers the theory and implementation of causal inference, as well as case studies and examples.
Provides a comprehensive introduction to statistical power analysis. It covers the theory and implementation of power analysis, as well as case studies and examples.
Save
Provides a comprehensive introduction to experimental design for the life sciences. It covers the theory and implementation of experimental design, as well as case studies and examples.
Save
Provides a comprehensive introduction to the R programming language and its applications in data science. It covers a wide range of topics, including data manipulation, data visualization, and statistical modeling.
Provides a comprehensive introduction to missing data in clinical studies. It covers the theory and implementation of missing data analysis, as well as case studies and examples.
Provides a comprehensive introduction to statistical inference and causal analysis. It covers the theory and implementation of statistical inference and causal analysis, as well as case studies and examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zgbzk5/advanced
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Effort
5 to 10 hours per week for 6 weeks
Level
Introductory
Via
edX
Institution
University of Canterbury
Instructor
Elena Moltchanova
Session
January 29, 2024
- August 28, 2024
Language
English
Transcript
English
This course belongs to a
collection
called
Statistical Analysis in R. It's comprised of two courses:
- Basics of Statistical Inference and Modelling Using R
- Advanced Statistical Inference and Modelling Using R (viewing)
Good to know
Expands upon fundamentals, covering a broader range of statistical techniques and concepts
Requires learners to come in with knowledge of basic linear regression and statistical inference
Led by an instructor with experience in teaching advanced statistical methods
Teaches tools and techniques that are widely used in industry and research
Includes hands-on labs and interactive materials to reinforce learning
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