Title  Indiana  Multiple Linear Regression and Logistic Regression 
Contributor/Contact  Patrick Monahan, PhD 
Institution  Indiana 
Acknowledgment  Please cite the appropriate contributors/authors/contacts when using or adapting these materials. 
Format  PDF slides 
Attachment  Regression 
URL_Web_Link 

Type of Course  Single Presentation 
Level of Course  Midlevel 
Audience  Clinical Researcher 
Topics Description 
Biostatistics Course for Health Care Providers: A Short Course Objectives of this course are: 1  Review simple linear regression 2  Describe assumptions, interpretations, and model checking for multiple linear regression 3  Describe assumptions, interpretations, and model checking for logistic regression 4  Discuss model selection procedures 
Software Program 

Datasets 

Data 

Keywords 
Simple linear regression Independent variable Dependent variable Predictor or explanatory variable Outcome or response variable Multiple linear regression Assumptions of linear regression Assumptions of multiple linear regression Example  multiple linear regression  Perceived barriers to mammography screening F test for significance Effect sizes Partial ttest Individual covariates Semipartial correlation coefficient Graphical model checking Nonnormality Nonlinearity Nonconstant variance Residuals Outliers Logistic regression Coding Wald chisquare tests Adjust odds ratio Likelihood ratio chisquare tests Model selection  explanatory Model selection  prediction 
See Also 

Type of Activity  Course Slides 
Disclaimer  The views expressed within CTSpedia are those of the author and must not be taken to represent policy or guidance on the behalf of any organization or institution with which the author is affiliated. 