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How to Download and Use Dea Solver Pro for Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a method for measuring the relative efficiency of decision making units (DMUs) that use multiple inputs and outputs. DEA can be used for benchmarking, performance evaluation, resource allocation, and policy making in various fields such as education, health care, banking, manufacturing, and public services.
Dea Solver Pro is a software that can help you perform DEA models using Microsoft Excel. It is developed by Professor Joe Zhu, who has years of DEA research and teaching experience. Dea Solver Pro is an Excel add-in that works under Excel XP-2019 and Excel 365 (Windows XP-Windows 8, 8.1, and 10). It uses Excel Solver as the engine for solving the DEA models.
Dea Solver Pro can handle various DEA models, such as CCR, BCC, directional distance function multiplier, additive, non radial, sbm, radial super efficiency, additive super efficiency, sbm super efficiency, cross efficiency, bootstrapping, Malmquist index, fuzzy DEA models, and more. It also provides graphical and tabular outputs for easy interpretation and analysis.
To download Dea Solver Pro, you need to visit the website of DEA Software [^1^] and order the software online. You can also try the free version of Dea Solver Pro before purchasing the full version. The free version has some limitations on the number of DMUs and variables that can be used.
After downloading Dea Solver Pro, you need to install it on your computer and activate it with a license key that you will receive by email. Then you can open Excel and load Dea Solver Pro from the Add-Ins menu. You will see a new toolbar with buttons for different DEA models. You can select the model you want to use and enter your data in a worksheet. Then you can click on the Solve button to run the DEA model and get the results.
For more details on how to use Dea Solver Pro, you can watch this video tutorial by Timbul Widodo [^3^] or read this user manual by Professor Joe Zhu . You can also contact the support team of DEA Software if you have any questions or problems with the software.Here are some more paragraphs for the article:
Benefits of DEA
DEA has many benefits for both researchers and practitioners who want to measure and improve the efficiency of DMUs. Some of the benefits of DEA are:
DEA does not require a priori specification of a mathematical form for the production function, which can be difficult or impossible to determine in some cases. DEA can handle any functional form as long as it satisfies some basic assumptions.
DEA can handle multiple inputs and outputs without requiring weights or prices for them. DEA can also handle qualitative or categorical variables by converting them into binary variables.
DEA can provide a comprehensive and detailed analysis of the sources and causes of inefficiency for each DMU. DEA can identify the best practices and benchmarks for each DMU, as well as the potential improvement targets and directions.
DEA can incorporate environmental factors, external variables, or undesirable outputs into the analysis by using different extensions and modifications of the basic DEA models.
DEA can measure the dynamic changes in efficiency over time by using panel data and applying models such as Malmquist index or window analysis.
DEA can be used for various purposes such as ranking, clustering, classification, discrimination, forecasting, and decision making.
Side Effects of DEA
While DEA is a powerful and flexible tool for efficiency analysis, it also has some limitations and drawbacks that need to be considered. Some of the side effects of DEA are:
DEA is sensitive to the choice of inputs and outputs, as well as the number and quality of data. If some relevant inputs or outputs are omitted, or if there are outliers or errors in the data, the results may be biased or misleading.
DEA is based on the assumption of constant returns to scale (CRS) or variable returns to scale (VRS), which may not hold in some situations. If there are increasing or decreasing returns to scale, DEA may overestimate or underestimate the efficiency scores.
DEA is a relative measure of efficiency, which means that it compares each DMU with a subset of DMUs that form its reference set. Therefore, DEA does not provide an absolute measure of efficiency or performance.
DEA does not account for uncertainty or randomness in the data, which may affect the efficiency scores. DEA also does not account for statistical noise or measurement errors in the data, which may cause variability in the results.
DEA does not test for statistical significance or hypothesis testing, which may limit its validity and reliability. DEA also does not provide confidence intervals or standard errors for the efficiency scores or other estimates.
DEA may suffer from the curse of dimensionality, which means that as the number of inputs and outputs increases, the number of efficient DMUs also increases, making it difficult to discriminate among them. 9160f4acd4