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LinkedIn Learning

Healthcare Analytics: Regression in R

via LinkedIn Learning

This course may be unavailable.

Overview

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Discover linear regression modeling and logistic regression modeling using R. Learn about how to prepare, develop, and finalize models using the forward stepwise modeling process.

Syllabus

Introduction
  • Welcome to the course
  • What you should know
  • Introduction to the course
  • Using the exercise files
1. Designing Your Research
  • Scientific method review
  • Using a cross-sectional approach
  • Reviewing existing literature for ideas
  • Dealing with scientific plausibility
  • Selecting a linear regression hypothesis
  • Selecting a logistic regression hypothesis
  • Installing necessary packages
2. Preparing for Linear Regression
  • Plots for checking assumptions in linear regression
  • Interpreting diagnostic plots
  • Categorization and transformation
  • Indexes
  • Quartiles
  • Ranking
  • Regression review
  • Preparing to report results
3. Beginning Linear Regression Modeling
  • Choices of modeling approaches
  • Overview of modeling process
  • Linear regression output
  • Models 1 and 2
  • Model metadata
4. Final Linear Regression Modeling
  • Beginning Model 3
  • Making a working Model 3
  • Finalizing Model 3
  • Looking at the final model
  • Fishing and interaction
  • Other strategies for improving model fit
  • Defending the final model
  • Presenting the final model
5. Preparing for Logistic Regression
  • Analogies to linear regression process
  • Parameter estimates in logistic regression
  • Odds ratio interpretation
  • Basic logistic code
  • Forward stepwise regression: First two rounds
  • Forward stepwise regression: Round 3
6. Developing the Logistic Regression Model
  • Running Model 1
  • Adding odds ratios to models
  • Model metadata
  • Forward stepwise: Round 2
  • Forward stepwise: Round 3
  • Using AIC to assess model fit
  • When to compare nested models
  • How to compare nested models
  • Models 1 and 2 presentation
  • Model 3 presentation
  • Interpreting the final model
Conclusion
  • Review of metadata
  • Review of the process
  • Next steps

Taught by

Monika Wahi

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