Numerical models: These models are utilized to approximate the behavior of complex systems by dividing them into smaller, more manageable components, which are then solved iteratively using computational methods. Examples of numerical models include the Finite Element Method (FEM) used in structural engineering, the Weather Research and Forecasting (WRF) model employed in weather prediction, and epidemiological models used to simulate the spread of diseases.
Analytics models can be classified based russia whatsapp number data on the number of variables in the model. In this regard, the data analytics models can be univariate, bivariate, or multivariate
Univariate analysis involves analyzing the pattern present in a single variable using measures of centrality (mean, median, mode, and so on) and variation (standard deviation, standard error, range, variance, and so on).
Bivariate analytics models: There are two variables wherein the analysis is related to cause and the relationship between the two variables. These two variables could be dependent or independent of each other. The correlation technique is the most used bivariate analysis technique.
Multivariate analytics models: This model is used for analyzing more than two variables. Commonly used multi-variate analytics models are Linear Regression, Logistic Regression, and Support Vector Machines.
Finally, regardless of the type of model selected, managing associated risks is crucial in analytics. From a risk perspective, analytics models can be classified into three main types.