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Statistical Model
A statistical model is a simplified way of describing how data are generated. It helps us separate what we can explain using known information from what we cannot explain perfectly. We use statistical models for three main purposes: (1) to understand ...
Empty Model
Empty Model (Null Model) An empty model is a statistical model that describes a quantitative outcome variable using only a single overall value, typically the mean of the response. It is called empty because it contains no explanatory (predictor) ...
Data Generating Process (DGP)
Data Generating Process (DGP) The Data Generating Process (DGP) refers to the underlying mechanism—real or hypothetical—that produces the data we observe. A DGP specifies how variables are related, how randomness enters the system, and how observed ...
R Objects
R Objects Overview In R, everything is an object. Data, functions, models, and even results of calculations are all stored as objects. Understanding R objects is fundamental to working with data, writing reproducible code, and performing statistical ...
Vectors
Vectors Vectors are one of the most basic and important data structures in R. A vector is an ordered collection of values of the same data type, such as numbers, characters, or logical (TRUE/FALSE) values. In statistics and data science, vectors are ...