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Multiple Linear Regression

Multiple Linear RegressionSong GeBSN, RN, PhD CandidateJohns Hopkins University School of Biostatistics for Evidence based PracticeLearning ObjectivesBy the end of this module, you will be able to:1. Articulate assumptions for Multiple Linear regression2. Explain the primary components of Multiple Linear regression3. Identify and define the variables included in the Regression equation4. Construct a Multiple Regression equation5. Calculate a predicted value of a dependent variable using a Multiple Regression equationLearning Objectives Cont d6. Distinguish between unstandardized (B) and standardized (Beta) Regression coefficients7. Distinguish between different methods for entering predictors into a Regression model (simultaneous, hierarchical and stepwise)8.

Multiple Linear Regression Models • We can get six critical pieces of information from an MLR: – The overall significance of the model – The variance in the dependent variable that comes from the set of independent variables in the model – The statistical significance of each individual independent variable (controlling for the others)

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