Overview
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Deepen your SAS knowledge by learning how to conduct a regression analysis of a health survey data center using this popular data analytics platform.
Syllabus
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Introduction
- Introduction to the course
- What you should know
- Linear regression and hypothesis review
- Plots for testing assumptions
- Stepwise linear regression modeling
- Basic PROC GLM code
- Reading PROC GLM output
- Linear regression model presentation
- Linear regression: Early models
- Linear regression: Round 1
- Linear regression: The final model
- Linear regression model metadata
- Linear regression model fit
- Interpreting linear regression model
- Hypothesis and odds ratio review
- Outcome distribution
- Basic PROC LOGISTIC code
- Basic PROC LOGISTIC output
- Stepwise logistic regression modeling
- Logistic regression: Early models
- Logistic regression: Round 1
- Logistic regression: The final model
- Logistic regression model metadata
- AIC and AUC for model fit
- Interpreting the logistic regression model
- Presenting linear regression models
- Excel for linear regression models
- Presenting logistic regression models
- Excel for logistic regression models
- Collinearity in stepwise regression
- Interaction review
- Interactions in linear regression
- Interactions in logistic regression
- Interactions: Stratum-specific estimates
- -2 log likelihood for model fit
- Categorizing continuous outcomes
- Categorizing continuous covariates
- Flags for ordinal value levels
- Strategically collapsing categories
- Choosing reference groups
- Describe your regression analysis
- Review of the process
- Next steps
