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.
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.
Machine learning involves solutions to predict scenarios based on past data. Logistic regression offers probability functions based on inputs and their corresponding output.
The Malmquist productivity index or more commonly malmquist Data Envelopment Analysis (DEA) was first incepted by the researcher Malmquist in 1953 as a quantity to be used in the analysis of consumption of inputs (Färe, Grosskopf, & Margaritis, 2011).
The purpose of this article is to explain the process of determining and creating stationarity in time series analysis. Creating a visual plot of data is the first step in time series analysis. Graphical representation of data helps understand it better.
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.
The present article takes up the datasheets for the unmatched post and pre or post design and illustrates the results with statistics. The present discussion will focus on the interpretation of the results.
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 VRS-DEA (Data envelopment analysis).