Question 1

What is Statistical Analysis Software?

Accepted Answer

Statistical analytics tools inform data-driven decisions, whether that be in a research or business context. Enterprises have started to recognize the importance of accessing and combining all business-critical data to get a better understanding of how a company performs. Connecting disparate systems to make data consumable is a complex process, but once done, businesses are enabled to utilize statistical analytics solutions along with other data analysis techniques. This helps them visualize and interpret the data in an easily digestible manner. A business cannot transform into a digitally native enterprise until it uses its data to make intelligent decisions. These data-driven decisions provide an important edge and help separate a business from its competitors.  
 Medium and large companies are facing unprecedented challenges when managing and analyzing data. Some of these challenges are the exponential growth of the volume of enterprise data and its increasing complexity caused by new technologies like social media and the internet of things (IoT). To address these challenges, companies need to gather and analyze large volumes of data, both structured and unstructured, from different sources. Companies need to find trends and patterns in historical data while identifying future opportunities and risks. They also cannot afford to spend a lot of time analyzing data which loses its relevance quickly. Advanced statistical procedures and visualization provide a robust, user-friendly, and integrated platform to understand business data and solve complex business and research problems. 
 What Types of Statistical Analysis Software Exist? 
 Statistical analysis comes in many different flavors with each type being appropriate or beneficial for different use cases. Although the methods are many, the ultimate goal of the analysis is broken into three types: descriptive, prescriptive, and predictive. Descriptive analysis simply looks at the data as it is, but does not refer to or recommend any future outcome. Prescriptive analysis uses historical data to recommend a particular outcome. Finally, predictive analysis uses historical data to predict future data or outcomes. Apart from these broad methods, statistical analysis software can perform the following types of analysis: 
 Regression analysis 
 This type of analysis allows for conducting various regression methods such as ordinary least squares (OLS), weighted least squares (WLS), or generalized linear model (GLM). 
 Predictive analytics 
 Predictive analytics mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. Using a range of statistical analysis and algorithms, analysts use predictive analytics products to build decision models which business managers use to plan for the best possible outcome. 
 Survival analysis 
 This type of analysis allows for the evaluation of durations, events, and reliability in relation to statistical analysis. 
 Time series analysis 
 Time series analysis allows users to identify patterns within massive, continuous time series data sets to perform reporting, forecasting, and predictive analysis. 
 Bayesian analysis 
 This method of statistical inference allows one to combine prior information about a statistical parameter with evidence from information contained in a sample to guide the statistical inference process. 
 Decision trees 
 One common type of classification model is a decision tree. These models predict a number of possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in pipeline, they can close $100,000 in revenue, but if they build $10 million in pipeline, they should be able to close $1 million in revenue.

Question 2

What are the Common Features of Statistical Analysis Software?

Accepted Answer

Statistical analysis tools allow users to mine and analyze structured or unstructured data. Through the process of analysis, data sets and visualizations are created from the compiled data. 
 Data preparation: To analyze the data, it must be properly cleaned and should be of high quality. This preparation consists of deduplication, cleansing, and appending the data for statistical analysis. Not all analysis is achieved at the surface. Instead, robust statistical analysis tools mine data from databases and prepare it for analysis. 
 Data sampling: Data sampling allows users to select samples of data for defined procedures. 
 Statistical modeling: Statistical modeling involves creating a mathematical model that embodies a set of statistical assumptions concerning the creation of sample data which may be used to get a better idea about the makeup and distribution of the data. 
 Hypothesis testing: Statistical analysis tools often provide hypothesis testing features to ensure the analysis is consistent with the data and correct based on predetermined factors. This helps the researcher, data scientist, or statistician evaluate the outcome based on their initial hypothesis. 
 Data visualization: In addition to numerical inputs and outputs, statistical analysis software frequently allows the user to visualize results by means of graphs, charts, and reports. This not only helps the end user better understand them but also aids with communicating these results with the broader company.

Question 3

What are the Benefits of Statistical Analysis Software?

Accepted Answer

Irrespective of the type of complex data manipulation or analysis being performed, statistical analysis tools enable statisticians or business professionals to find correlation, regression, analysis of variance, and more with ease. With these insights in hand, businesses can make sense of their data and make informed decisions based on it. These tools help identify duplicates and unusual cases that may arise during data cleaning and data curation and get detailed data analysis reports. 
 Manage large volumes of data: Statistical analysis software gives one the ability to easily prepare, blend, and analyze company data using a repeatable workflow, then deploy and share analytics at scale for deeper insights. 
 Gain insight into company data: Through data mining and statistical analysis, especially when coupled with other technology such as machine learning, data professionals are able to gain insights into data. 
 Better understand potential outcomes and scenarios: With statistical analysis, especially of the predictive variety, businesses better understand how different variables will affect them and are able to plan accordingly.

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G2 Grid® for Statistical Analysis Software

Zometric Statistical AI

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