Category: Learning modules »

How to apply linear discriminant analysis?

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 »

Malmquist productivity Index test of healthcare sector in India and interpretations

Malmquist productivity index evaluates the efficiency change over time as mentioned by Färe, Grosskopf, & Margaritis, (2011). However, Malmquist productivity index literature has been uneven with some authors assuming constant returns to scale and others allowing for variable returns to scale. This article will present the interpretations of productivity index from Malmquist Data Envelopment Analysis (DEA). Read more »

How to perform cluster analysis?

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 »

How to apply logistic regression in a case?

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.

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Problem of non-stationarity in time series analysis in STATA

The previous article discussed the process for setting the ‘Time variable’ while conducting time series analysis in STATA. The purpose of this article is to explain the process of determining and creating stationarity in time series analysis. Read more »

How to conduct path analysis?

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 »

Interpreting summary of peers and projected values in VRS-DEA

This is the continued article of the interpretations for variable returns to scale (VRS) from the last article. However, this article is about the summary of peers and the rest of the analysis conducted for VRSDEA (Data envelopment analysis). Read more »

Data entry formats in CMA

The earlier article (Introduction to CMA) gave a brief overview of the various functionalities offered by the CMA software. This article acquaints the user with the procedure of selecting appropriate data entry formats. Read more »

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