Linear Discriminant Analysis | DataPandit Analytics Platform
A web-based solution for Linear Discriminant Analysis. It is widely used to solve classification problems where one needs to segregate different samples into their respective class or group. This technique works well when basic assumptions related to data normality are followed by the data. An easy demonstration of the app can be seen here
Principle Component Analysis | DataPandit Analytics Platform
A web-based solution useful to understand data groupings in highly dimensional data. PCA has an advantage when the number of columns in the dataset is greater than the number of rows in the dataset. It works well with a high level of multi-collinearity within the factors. Typically, used for the classification of materials. Here is an easy demonstration of the PCA application.
Partial Least Square Regression | DataPandit Analytics Platform
A web-based solution useful if data has a high degree of multicollinearity within the factors and you need quantitative predictions from the model. Typically used in Process Analytical Technology (PAT) applications for real-time process control. A PAT application of this app is demonstrated here.
Principle Component Regression | DataPandit Analytics Platform
A web-based solution which can serve as an alternative to Multiple Linear Regression and Partial Least Square Regression. It can overcome the multicolinearity problem be used for building quantitative chemometric models.
Multiple Linear Regression | DataPandit Analytics Platform
Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable when the variables follow the normality assumptions.
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