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Title: Indiana - Multiple Linear Regression and Logistic Regression

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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 Mid-level
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 t-test
Individual covariates
Semi-partial correlation coefficient
Graphical model checking
Non-normality
Non-linearity
Non-constant variance
Residuals
Outliers
Logistic regression
Coding
Wald chi-square tests
Adjust odds ratio
Likelihood ratio chi-square 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.
Topic revision: r3 - 08 Oct 2012 - 14:44:50 - MaryBanach
 

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