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LinkedIn Learning

Healthcare Analytics: Regression in R

via LinkedIn Learning

This course may be unavailable.

Overview

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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