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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. Identify strategies to assess model fit9. Interpret and report the results of Multiple Linear Regression analysisReview of lecture two weeks ago Linear Regression assumes a Linear relationship between independent variable(s) and dependent variable Linear Regression allows us to predict an outcome based on one or several predictors Linear Regression allows us to explainthe interrelationships among variables Linear Regression is a parametric testHow to choose X and Y?

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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