Share Description
In this course, you will :
- helps you to expand your SAS knowledge by demonstrating how to use the platform to perform a regression analysis on a health survey data centre.
- demonstrates how to perform regression analyses and present your model's results in tables She demonstrates how to create and present a linear regression model using PROC GLM as part of a hypothesis-driven analysis, as well as how to create a logistic regression model using PROC GENMOD and PROC LOGISTIC, and how to present and interpret your linear and logistic regression models.
- goes over regression issues and gives a few helpful hints
Syllabus :
1. Preparing for Linear Regression
- Linear regression and hypothesis review
- Plots for testing assumptions
- Stepwise linear regression modeling
- Basic PROC GLM code
- Reading PROC GLM output
2. Linear Regression Modeling
- 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
3. Preparing for Logistic Regression
- Hypothesis and odds ratio review
- Outcome distribution
- Basic PROC LOGISTIC code
- Basic PROC LOGISTIC output
- Stepwise logistic regression modeling
4. 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
5. Model Presentation
- Presenting linear regression models
- Excel for linear regression models
- Presenting logistic regression models
- Excel for logistic regression models
6. Issues in Regression
- Collinearity in stepwise regression
- Interaction review
- Interactions in linear regression
- Interactions in logistic regression
- Interactions: Stratum-specific estimates
- -2 log likelihood for model fit
7. Regression Tips
- Categorizing continuous outcomes
- Categorizing continuous covariates
- Flags for ordinal value levels
- Strategically collapsing categories
- Choosing reference groups
- Describe your regression analysis
