Multiple Linear Regression

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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. Distinguish between unstandardized (B) and standardized (Beta) Regression coefficients7.

2. The dependent variable must be of ratio/interval scale and normally distributed overall and normally distributed for each value of the independent variables 3. For every value of X, the distribution of Y scores must have approximately equal variability (homoscedasticity) 4. The relationship between X and Y must be linear 5.

  Variable, Distributed, Normally, Normally distributed