Deep Dive into Regression Modeling Approaches and Covariate Analysis
Explore various regression modeling approaches such as CART, GLM, GAM, and more, understanding their responses, covariates, and model building techniques for optimal results in data analysis and predictive modeling.
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Regression Modeling Approaches We re about to explore approaches to regression/covariate modeling: CART: Classification and Regression Trees GLM: Generalized Linear Models GAM: Generalized Additive Models HEMI 2: Hyper-envelope Modeling Interface MaxEnt: Maximum Entropy namNm15
CART: Response: Categorical Covariates: Categorical or Continuous GLM: Response: Binary or Continuous (known function: linear, gamma, binomial ) Covariates: Continuous GAM: Response: Virtually any continuous Covariates: Continuous HEMI 2 & MaxEnt: Response: Occurrences (points) Covariates: Continuous (typical) or Categorical namNm15
Response Drives the Method Occurrences only (point density): Habitat: MaxEnt, HEMI 2 Density estimators, clustering Binary (presence/absence): Binomial, CART Categorical: CART Continuous: Linear Regression: Linear GLM: Linear, Poisson, Gamma GAM: Virtually any continuous namNm15
Building Models Selecting the method Selecting the covariates/predictors ( Model Selection ) Optimizing the coefficients/parameters of the model namNm15 9bytez.com:Old School Hobbies: Building Models by Hand
