Statistics is the combination of science and mathematics which involves data collection, analysis and interpretation. The main purpose of statistics assignments is making the students understand how the data is analyzed and interpreted. For analyzing and interpreting the statistical data, there are many tools that can be very helpful not only for students but also for experts and professionals. This article will give you a review by an assignment writing service about some popular tools for statistical analysis.
SPSS:
Statistical Packages for Social Science abbreviated as SPSS is one of the most popular statistical tools. It was first developed by SPSS Inc. and its original name was under the umbrella. Late in 2009 IBM acquired it and in 2015 named it as IBM SPSS. It is the most famous statistical tool for data handling and storage. It offers you two ways for data handling; interactive batches and non-interactive batches. Its open-source version is also available which is called PSPP. This version helps in professional handling of large amounts of data. It is very fast and has reliable performance. It is used for data computation, storage and formats. It also built the process in batches, helps in memory management, use for probabilistic theories, reasoning, discrete structures, and databases.
MS Excel:
Excel is available in almost all computers and students are taught its usage from very beginning classes. It’s the most commonly used software for statistical purposes. It is very easy and friendly to use and has a very good graphical interface. You can store data in tabular form. It also has storing and data filtering options. The best feature of MS Excel is pivot tables and data import and export options. It also offers creation of custom functions through programming abilities. You can also make reports through excel. It reduces data redundancy.
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R Foundation for Statistical Computing:
R Foundation for Statistical Computing commonly known as R is a free software of statistical analysis. It is very commonly used in social sciences for data handling and analysis. It has a steep learning curve and its different plugins are introduced from time to time which shows that it is a process of creation and improvement. In the R Foundation for Statistical Computing, you have to create your own coding.
MATLAB:
MATLAB is more associated with engineering. It has various toolboxes available for helping in research questions but this software is quite difficult to use as software for data handling. But still, it offers flexibility for what you want to do with your data but for that, you must now do the coding.
Statistical Analysis Software (SAS):
SAS also known as Statistical Analysis Software is used for advanced analysis. It has two options for data analysis. Either you can use GUI or you can create scripts. It also generates graphs and charts. But like MATLAB and R Foundation for Statistical Computing, you need to learn coding for efficiently using this software.
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Minitab:
This software is used for both basic and advanced level statistical data handling. It has both GUI and scripted command options. It is helpful for both new learners as well as experts for complex data.
GraphPad Prism:
This software works best for statistical data analysis used for biological researches. It is much similar to SPSS and easy to use. It is very important to use the right tool for statistical data analysis as the result of research depends wholly solely on the methods used for research and interpretation techniques and tools involved. There is a variety of software present in the market but choosing the right one depends on the user’s knowledge and familiarity about the usage of software and the compatibility of the features of the software with factors involved in your research topic.