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Data Analysis: Statistical Analysis

Learn how to interpret and explore data to use data confidently, find answers and make smart decisions.

What is Statistical Analysis? Why is it important?

Statistical analysis is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions.It involves  inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information and informing conclusions useful not only in business but interdisciplinary domains.

Software and Tools available

1. SPSS 

(Statistical Package for the Social Sciences) is perhaps the most widely used statistics software package. SPSS offers the ability to easily compile descriptive statistics, parametric and non-parametric analysis, as well as graphical depictions of results through the graphical user interface (GUI). It also includes the option to create scripts to automate analysis, or to carry out more advanced statistical processing.(Proprietary)

Access IBM SPSS Packages - Available in the UAlbany Library Computing Sites     

Learn SPSS : Support Guide


2. R

R is a free statistical software package that is widely used in multidisciplinary fields. Toolboxes (essentially plugins) are available for a great range of applications, which can simplify various aspects of data processing. While R is a very powerful software, it also has a steep learning curve, requiring a certain degree of coding. It comes with an active community engaged in building and improving R and the associated plugins.

Access R Studio : Available in the UAlbany Library Computing Sites

Download - R Studio                         Get Started : Support Tutorials 


3. Stata 

The name Stata is a syllabic abbreviation of the words statistics and data. Stata's capabilities include data management, statistical analysis, graphics, simulations, regression, and custom programming.( Proprietary software)

Access R Studio : Available in the UAlbany Library Computing Sites       

Get Started : Support Tutorials 


4. Python

Python is an open source general-purpose interpreted, interactive, object-oriented, and high-level programming language widely used in Data Science with many packages for scientific computing and statistical analysis 

Access R Studio : Available in the UAlbany Library Computing Sites or Download online : Python

Learn Python : Support Tutorials 

A good resource to learn python is the Google's Python Class


5. Microsoft Excel

While not a cutting-edge solution for statistical analysis, MS Excel does offer a wide variety of tools for data visualization and simple statistics. It’s simple to generate summary metrics and customizable graphics and figures, making it a usable tool for many who want to see the basics of their data. As many individuals and companies both own and know how to use Excel, it also makes it an accessible option for those looking to get started with statistics.

Access R Studio : Available in the UAlbany Library Computing Sites