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
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Introduction
- Welcome to the course
- What you should know
- Introduction to the course
- Using the exercise files
- 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
- Plots for checking assumptions in linear regression
- Interpreting diagnostic plots
- Categorization and transformation
- Indexes
- Quartiles
- Ranking
- Regression review
- Preparing to report results
- Choices of modeling approaches
- Overview of modeling process
- Linear regression output
- Models 1 and 2
- Model metadata
- 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
- 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
- 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
- Review of metadata
- Review of the process
- Next steps
