Regression Models in Healthcare
Master advanced multivariate stats for healthcare using R-analyze trends, interactions, outliers, and logistic regression.
Master advanced multivariate stats for healthcare using R-analyze trends, interactions, outliers, and logistic regression.
In this comprehensive course from MGH Institute, you'll dive into advanced multivariate statistical methods essential for healthcare data analysis. The curriculum focuses on practical application while providing sufficient theoretical foundation to understand the mathematical underpinnings of statistical techniques. Over four structured modules, you'll learn to implement and interpret various regression models using the R statistical programming software. Starting with non-linear trends analysis, you'll progress to understanding interacting variables and identifying outliers that may affect your results. The course then transitions to logistic regression techniques, including how to perform diagnostic tests to validate your models. You'll also explore specialized variants such as ordinal, multinomial, and Poisson logistic regression. Each concept is reinforced through written content, video lectures, step-by-step activities, and assessments that mimic real-world healthcare scenarios. By completion, you'll be equipped with the analytical tools needed to interpret healthcare data effectively and make data-informed decisions in professional settings. This course is an integral component of the MicroMasters in Healthcare Data Analytics Toolkit program, providing essential skills for healthcare data analysis professionals.
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What you'll learn
Use nonlinear regressions with quadratic and logarithmic dependent and independent variables
Implement and interpret interactions between variables in regression models
Identify and address potentially problematic data points in regression analysis
Apply logistic regression models and interpret their results effectively
Perform diagnostic tests to determine the validity of logistic regression models
Utilize specialized variants including ordinal, multinomial, and Poisson logistic regression
Skills you'll gain
This course includes:
PreRecorded video
Module Quizzes (60% - 15% for each of 4 quizzes), Summative Assessment (40%)
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This comprehensive course explores advanced multivariate statistical methods essential for healthcare data analysis. Students will learn to implement and interpret various regression models using R statistical programming software. The curriculum covers non-linear trends analysis, interacting variables, outlier identification, and logistic regression techniques including ordinal, multinomial, and Poisson variants. Each module combines theoretical foundations with practical applications specific to healthcare scenarios. The course emphasizes both the mathematical understanding of statistical methods and their real-world implementation. Students will gain skills in performing diagnostic tests to validate regression models and interpreting results to make data-informed decisions in healthcare settings. The course features written content, video lectures, hands-on activities, and assessments that reinforce learning objectives. As part of the MicroMasters in Healthcare Data Analytics Toolkit program, this course provides essential analytical capabilities for healthcare professionals working with complex datasets.
Non-Linear Trends
Module 1
Interacting Variables and Finding Outliers
Module 2
Logistic Regression
Module 3
Logistic Regression Variants
Module 4
Fee Structure
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