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From the process of installation of DEA-Solver to the computation of the analysis, the DEA process undergoes a various set of challenges. The first challenge incurs at the time of the installation itself, as it requires to be manually included in MS Excel.
DEA module, getting started with DEA
In the previous article, “Using Data Envelopment Analysis (DEA) program for measuring efficiency” the process to download Data Envelopment Analysis program (DEAP) was explained and the case problem in hand was defined. The first step is to understand how to import data to DEAP.
DEA module, getting started with DEA
Autoregressive Integrated Moving Average (ARIMA) is the statistical tool with a standard structure which though is simpler but provides skillful information about the stock market.
stock categorization, stock market analysis, stock market page intro, time series analysis, trend analysis
E-views is the statistical package widely used by economists, researchers and analysts for statistical analysis, forecasting and model simulations.
eviews, qualitative analysis
Machine learning has become an indispensable tool in bioinformatics research. Biomarkers act as indicators of any biological state of the body. It can help to detect different kinds of diseases.
Importance of Molecular Biomarkers in Non-communicable diseases
The database for annotation, visualisation and integrated discovery (DAVID) is a bioinformatics tool that consists of an integrated biological database and analytical tools.
Importance of Molecular Biomarkers in Non-communicable diseases
Matrix Laboratoryn (MATLAB) is a high-level language that is matrix based and includes an interactive environment for numerical computation, visualization,development and programming which was originally written in FROTRAN (Formual Translation). The present module is based on R 2017a version and is available for 64 bit Windows, Mac and Linux operating system. All the MATLAB variables […]
Matlab Module
R software has plenty of packages and is unique in handling big data. Therefore it can handle both the structured and unstructured data. This makes it suitable for big data analysis also.
Need of Big data in the Indian banking sector, R-module