In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.
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In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.
In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.
This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).
The course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Non-Linear Trends
- Module 2: Interacting Variables and Finding Outliers
- Module 3: Logistic Regression
- Module 4: Logistic Regression Variants
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Learning objectives
- By the end of this course, you will be able to:
- Use nonlinear regressions with quadratic and logarithmic dependent and independent variables.
- Use interactions between variables in regression models and interpret the results.
- Find potentially problematic data points in a regression model.
- Implement logistic regression models and interpret their results.
- Perform diagnostic tests for logistic regression models to determine their validity.
- Use ordinal, multinomial, and poisson logistic regression models and interpret their results.
- By the end of this course, you will be able to:
- Use nonlinear regressions with quadratic and logarithmic dependent and independent variables.
- Use interactions between variables in regression models and interpret the results.
- Find potentially problematic data points in a regression model.
- Implement logistic regression models and interpret their results.
- Perform diagnostic tests for logistic regression models to determine their validity.
- Use ordinal, multinomial, and poisson logistic regression models and interpret their results.
Syllabus
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Activities
Review foundational concepts in statistics prior to starting the course
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Refreshing your knowledge of foundational statistics will provide a strong foundation for success in this course.
Browse courses on
Statistics
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-
Review basic concepts of probability and statistics.
-
Practice solving basic statistics problems.
-
Identify any areas where you need additional support.
Identify a mentor or expert in the field of healthcare data analysis
Show steps
Having a mentor can provide you with valuable guidance and support throughout your learning journey.
Show steps
-
Identify potential mentors who have experience in healthcare data analysis.
-
Reach out to mentors and express your interest in learning from them.
-
Establish regular communication and seek guidance on your progress.
Organize and review course materials
Show steps
By organizing and reviewing course materials regularly, you can improve your retention and understanding of the course content.
Show steps
-
Create a system for organizing your notes, assignments, and other course materials.
-
Review your course materials on a regular basis.
-
Identify any areas where you need further clarification or support.
Five other activities
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Practice problems related to nonlinear regressions with quadratic and logarithmic dependent and independent variables
Show steps
Practice problems will reinforce your understanding of the concepts covered in Module 1.
Browse courses on
Multivariate Statistics
Show steps
-
Identify the type of nonlinear regression model (quadratic or logarithmic) that is appropriate for the given data.
-
Estimate the parameters of the nonlinear regression model using statistical software.
-
Interpret the results of the nonlinear regression analysis.
Discuss the use of interactions between variables in regression models
Show steps
Discussing with peers will help you clarify your understanding of Module 2 concepts and identify potential challenges.
Show steps
-
Identify the variables that might interact with each other in the given regression model.
-
Create interaction terms between the variables.
-
Interpret the results of the regression analysis with interaction terms.
Explore online tutorials on logistic regression variants
Show steps
Online tutorials will provide additional resources to enhance your understanding of the different types of logistic regression models covered in Module 4.
Browse courses on
Multivariate Statistics
Show steps
-
Search for online tutorials on ordinal logistic regression.
-
Review the concepts of ordinal logistic regression.
-
Apply ordinal logistic regression to a real-world dataset.
-
Repeat the same steps for multinomial and Poisson logistic regression.
Create a presentation on logistic regression models
Show steps
Creating a presentation will help you synthesize the information covered in Module 3 and develop strong communication skills.
Browse courses on
Logistic Regression
Show steps
-
Review the concepts of logistic regression.
-
Identify the steps involved in building a logistic regression model.
-
Apply logistic regression to a real-world dataset.
-
Create presentation slides that effectively communicate the key concepts.
-
Deliver the presentation to an audience.
Contribute to open-source projects related to healthcare data analysis
Show steps
Contributing to open-source projects can provide you with practical experience and help you build your portfolio.
Show steps
-
Identify open-source projects related to healthcare data analysis.
-
Review the project documentation and identify areas where you can contribute.
-
Make meaningful contributions to the project.
-
Collaborate with other contributors and learn from their experiences.
Review foundational concepts in statistics prior to starting the course
Show steps
Refreshing your knowledge of foundational statistics will provide a strong foundation for success in this course.
Browse courses on
Statistics
Show steps
- Review basic concepts of probability and statistics.
- Practice solving basic statistics problems.
- Identify any areas where you need additional support.
Identify a mentor or expert in the field of healthcare data analysis
Show steps
Having a mentor can provide you with valuable guidance and support throughout your learning journey.
Show steps
- Identify potential mentors who have experience in healthcare data analysis.
- Reach out to mentors and express your interest in learning from them.
- Establish regular communication and seek guidance on your progress.
Organize and review course materials
Show steps
By organizing and reviewing course materials regularly, you can improve your retention and understanding of the course content.
Show steps
- Create a system for organizing your notes, assignments, and other course materials.
- Review your course materials on a regular basis.
- Identify any areas where you need further clarification or support.
Practice problems related to nonlinear regressions with quadratic and logarithmic dependent and independent variables
Show steps
Practice problems will reinforce your understanding of the concepts covered in Module 1.
Browse courses on
Multivariate Statistics
Show steps
- Identify the type of nonlinear regression model (quadratic or logarithmic) that is appropriate for the given data.
- Estimate the parameters of the nonlinear regression model using statistical software.
- Interpret the results of the nonlinear regression analysis.
Discuss the use of interactions between variables in regression models
Show steps
Discussing with peers will help you clarify your understanding of Module 2 concepts and identify potential challenges.
Show steps
- Identify the variables that might interact with each other in the given regression model.
- Create interaction terms between the variables.
- Interpret the results of the regression analysis with interaction terms.
Explore online tutorials on logistic regression variants
Show steps
Online tutorials will provide additional resources to enhance your understanding of the different types of logistic regression models covered in Module 4.
Browse courses on
Multivariate Statistics
Show steps
- Search for online tutorials on ordinal logistic regression.
- Review the concepts of ordinal logistic regression.
- Apply ordinal logistic regression to a real-world dataset.
- Repeat the same steps for multinomial and Poisson logistic regression.
Create a presentation on logistic regression models
Show steps
Creating a presentation will help you synthesize the information covered in Module 3 and develop strong communication skills.
Browse courses on
Logistic Regression
Show steps
- Review the concepts of logistic regression.
- Identify the steps involved in building a logistic regression model.
- Apply logistic regression to a real-world dataset.
- Create presentation slides that effectively communicate the key concepts.
- Deliver the presentation to an audience.
Contribute to open-source projects related to healthcare data analysis
Show steps
Contributing to open-source projects can provide you with practical experience and help you build your portfolio.
Show steps
- Identify open-source projects related to healthcare data analysis.
- Review the project documentation and identify areas where you can contribute.
- Make meaningful contributions to the project.
- Collaborate with other contributors and learn from their experiences.
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