Introduction to Logistic Regression Analysis
Logistic regression is a type of regression modeling that is used when the dependent variable is binary. The workshop is designed for people who would like to learn how to implement logistic regression analysis in their research. No previous experience with or knowledge about logistic regression is necessary, although participants should be familiar with multiple linear regression. Those who have used logistic regression before and would like to learn more about it are welcome to attend.
The emphasis will be on deciding when logistic regression is appropriate, the terminology used in logistic regression, and the interpretation of the results. Experience with any specific statistical software package will not be required, although participants should be familiar with using statistical software to analyze data. Output from software will be used in explaining how to interpret results.
After attendance at the workshop, you should be able to:
- Decide whether logistic regression is appropriate for your data
- Interpret results from a logistic regression analysis
- Test which logistic regression model best fits the data
- Check whether the assumptions are being met
- Understand the terminology used in software manuals so that you can choose the appropriate options for a particular analysis.
Fee: None, but registration is required.
Offered by: Simona Despa, Cornell Statistical Consulting Unit
For times and locations of upcoming workshops, please see the Workshop Schedule.
Data for Logistic Regression Workshop: