Linear discriminant model is a multivariate model. It is used for modeling the differences in groups. In this model a categorical variable can be predicted through continuous or binary dependent variable. Linear discriminant analysis allows researchers to separate two or more classes, objects and categories based on the characteristics of other variables. It is a classification technique like logistic regression. However the main difference between discriminant analysis and logistic regression is that instead of dichotomous variables, discriminant analysis involves variables with more than two classifications. Read more »
While many statistical methods in machine learning are used either to predict or analyse trends in the data, cluster analysis is used for organizing the data. It is a process of grouping observations of similar kinds within a large population. Therefore, it tries to identify homogenous groups of cases. Read more »
Machine learning involves solutions to predict scenarios based on past data. Logistic regression offers probability functions based on inputs and their corresponding output. In short, the dependent variable is a classification variable.
Path analysis is a graphical representation of multiple regression models. In this analysis, the graphs represent the relationship between dependent and independent variables with the help of square and arrows. These graphs show the causation between different variables of a regression model. Read more »
Survival analysis is a method under predictive modeling where the dependent variable is time. Therefore, it involves time-to-event prediction modeling. The methodology is that the outcome variable is time until the occurrence of a certain event. The response of the event is known as the survival time, failure time or event time. Read more »
Regression analysis is a statistical tool to study the relationship between variables. These variables are the outcome variable and one or more exposure variables. In other words, regression analysis is an equation which predicts a response from the value of a certain predictor. In a linear regression analysis model the regression function µY(X1…….Xk) is a linear function of the unknown parameters. Read more »