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In this course, you will learn :
- How to perform a forward stepwise modelling process using the publicly available Behavioral Risk Factor Surveillance Survey (BRFSS) dataset in this course.
- How to design your research by selecting a hypothesis based on scientific plausibility.
- The steps of preparing, developing, and finalising a linear regression model as well as a logistic regression model.
- How to interpret diagnostic plots, improve model fit, and compare models, among other things.
Syllabus :
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
- Ranking
- Regression review
- Preparing to report results
3. Beginning Linear Regression Modeling
- Choices of modeling approaches
- Overview of modeling process
- Linear regression output
- Model metadata
4. Final Linear Regression Modeling
- Beginning
- Making a working
- Finalizing
- 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
- Adding odds ratios to models
- Model metadata
- Using AIC to assess model fit
- When to compare nested models
- How to compare nested models
- Interpreting the final model
