A Model is a simplification of reality. It doesn’t capture all the complexities of the actual phenomenon but focuses on the most relevant aspects for the purpose of the analysis.
Variables: Can be independent (predictors) or dependent (responses). Independent variables are those considered as potential causes or influences, while dependent variables are the effects or outcomes that are studied.
Parameters: Coefficients in the model that quantify the relationship between the variables. These parameters are estimated from the data.
Use of Data: Statistical models are built and validated using data. This data can come form experiments, surveys, historical records, etc…
#Types of Models
There are various types of models, including linear, non-linear, parametric, non-parametric, etc. The choice of the model depends on the nature of the data and the phenomenon being studied. In this course we will discuss the fundamentals of linear regression and binary logistic regression models.
Linear Regression is used to model the relationship between a continuous dependent variable and one or more independent variables. The model assumes a linear relationship between the variables. Linear regression is then used with a continuous dependent variable.
Binary Logistic Regression is used when the dependent variable is binary (i.e., it has only two possible outcomes, often coded as 0 and 1). It models the probability of a particular outcome as a function of the independent variables. Binary logistic regression is then used with a binary discrete dependent variable.
Introduction to Simulation and Modelling