Week 2 – Assignment: Evaluate Tools Used for Statistical Applications
For this assignment, you must write a paper evaluating a minimum of three tools that can be used for statistical analysis and reporting.
Week 2 – Assignment: Evaluate Tools Used for Statistical Applications
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| For this assignment, you must write a paper evaluating a minimum of three tools that can be used for statistical analysis and reporting. Your evaluation should include the following:Open source tools (e.g., Python, R, Octave, QGIS) and commercial software (e.g., SPSS, SAS, Tableau, ArcGIS).Discussion of the characteristics of each tool with a focus on how they could be used.Analysis of the trade-offs between the tools.Infographics serve as visual aids where information is presented in a way that makes it easily discernable. You can conduct a quick internet search to find examples of infographics, or you can view an example provided under this week’s Weekly Resources and Assignments area.Conclusion with tool recommendations. Length: 3-4 page paper, not including title and reference pages, plus infographicReferences: Include a minimum of 5 scholarly resources.The completed assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. The content should reflect scholarly writing and current APA standards and should adhere to Northcentral University’s Academic Integrity Policy.————————-Technology Resources for Statistical ApplicationsData analysis software is a requirement for processing large amounts of data and/or implementing complex models. Even basic descriptive analyses with small data sets should be conducted using the software in order to avoid human error and to dramatically increase efficiencies and productivity.Numerous tools for analyzing and visualizing data exist, which can make the selection of a specific tool or tools overwhelming. Open source tools, such as Python and R, are free, but they can be difficult to use given that users will likely encounter errors when trying to install various packages with deprecated libraries. Octave is an open-source alternative to MATLAB, but it is not very common. Some of the most common commercial software for data analysis include SAS, SPSS, and MATLAB. SPSS is the most user-friendly because you are not required to write code; it is point and click-based. While SAS provides a user interface, it is predominately intended for coding. SAS licenses are quite expensive, and therefore SAS is typically used for enterprises. SPSS is more reasonable for an individual user. With that being said, you should do your own research on the available tools and determine which option is the best choice for your own personal needs.For special types of data, such as geospatial data, there are specific types of tools available. ArcGIS is a commercial software tool that is very popular. An open-source alternative, that works with Python or Anaconda, is QGIS. On the data visualization side, tools such as PowerBI, Tableau, and Birst are popular commercial packages. With that being said, you are able to create visualizations as part of the data analysis tools mentioned. Therefore, you don’t necessarily need to purchase a separate visualization package unless there are specific features that you require that are not available in the data analysis tools.Spotlight on Skills: Creating an InfographicTo view the Spotlight on Skills, go to the Course Resources module in the table of contents and click on Spotlight on Skills.Review the weekly resources (as well as previous resources, as required) to prepare for this week’s assignments. The resources may include textbook reading assignments, journal articles, websites, links to tools or software, videos, handouts, rubrics, etc.Ressources: Anaconda Distribution — Anaconda documentation (continuum.io) Orcid Research IG letter. (wikimedia.org) |
Answer preview For this assignment, you must write a paper evaluating a minimum of three tools that can be used for statistical analysis and reporting.
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