Introduction
Introduction to the course
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What you should know
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1. Preparing for Linear Regression
Linear regression and hypothesis review
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Plots for testing assumptions
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Stepwise linear regression modeling
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Basic PROC GLM code
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Reading PROC GLM output
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2. Linear Regression Modeling
Linear regression model presentation
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Linear regression: Early models
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Linear regression: Round 1
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Linear regression: The final model
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Linear regression model metadata
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Linear regression model fit
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Interpreting linear regression model
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3. Preparing for Logistic Regression
Hypothesis and odds ratio review
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Outcome distribution
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Basic PROC LOGISTIC code
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Basic PROC LOGISTIC output
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Stepwise logistic regression modeling
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4. Logistic Regression Modeling
Logistic regression: Early models
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Logistic regression: Round 1
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Logistic regression: The final model
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Logistic regression model metadata
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AIC and AUC for model fit
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Interpreting the logistic regression model
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5. Model Presentation
Presenting linear regression models
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Excel for linear regression models
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Presenting logistic regression models
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Excel for logistic regression models
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6. Issues in Regression
Collinearity in stepwise regression
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Interaction review
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Interactions in linear regression
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Interactions in logistic regression
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Interactions: Stratum-specific estimates
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-2 log likelihood for model fit
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7. Regression Tips
Categorizing continuous outcomes
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Categorizing continuous covariates
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Flags for ordinal value levels
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Strategically collapsing categories
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Choosing reference groups
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Describe your regression analysis
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Conclusion
Review of the process
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Next steps
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Ex_Files_SAS_Regression_Healthcare.zip
(5.8 MB)