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Multiple Linear Regression - Blackboard Learn

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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.

• The overall variance explained by the model (R2) as well as the unique contribution (strength and direction) of each independent variable can be obtained • In MLR, the shape is not really a line. If there are three variables, the shape is a plane, and if there are four or more variables, it is impossible to visualize or graph.

  Variance

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