Model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data Formally the model for multiple linear regression given ID: 632627
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Outlier DetectionSlide2
Multiple linear regression (MLR)
Model
the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data.Formally, the model for multiple linear regression, given n observations, is Some of its uses:Risk scoresPredictive modelingMore sophisticated methods (Logistic and Poisson regression)Slide3
Terminology
What’s an outlier?
Outlier is an observation that is numerically distant from the rest of the data.What’s a residual? The difference between the observed value of the dependent variable (y) and the predicted value (ŷ).Slide4
Residual analysis
Some of its uses:
Validating model accuracyLooking for patterns in the errorsPoints with high leverageMulticollinearity (through VIF analysis)Identifying outliers