Statistical Analysis

the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics are applied every day – in research, industry and government – to become more scientific about decisions that need to be made. For example:

• Manufacturers use statistics to weave quality into beautiful fabrics, to bring lift to the airline industry and to help guitarists make beautiful music.
• Researchers keep children healthy by using statistics to analyze data from the production of viral vaccines, which ensures consistency and safety.
• Communication companies use statistics to optimize network resources, improve service and reduce customer churn by gaining greater insight into subscriber requirements.
• Government agencies around the world rely on statistics for a clear understanding of their countries, their businesses and their people.

Uses of Statistical Analysis

Statistical Analysis may be used to:

• Summarize the data. For example, make a pie chart.
• Find key measures of location. For example, the mean tells you what the average (or “middling”) number is in a set of data.
• Calculate measures of spread: these tell you if your data is tightly clustered or more spread out. The standard deviation is one of the more commonly used measures of spread; it tells you how spread out your data is about the mean.
• Make future predictions based on past behavior. This is especially useful in retail, manufacturing, banking, sports or for any organization where knowing future trends would be a benefit.
• Test an experiment’s hypothesis. Collecting data from an experiment only tells a story when you analyze the data. This part of statistical analysis is more formally called “Hypothesis Testing,” where the null hypothesis (the commonly accepted theory) is either proved or disproved.

Types of Statistical Analysis

The two main types of statistical analysis are descriptive and inferential. However, there are other types that also deal with many aspects of data including data collection, prediction, and planning.

Statistical Analysis Software

Since not everyone is a mathematic genius who is able to easily compute the needed statistics on the mounds of data a company acquires, most organizations use some form of statistical analysis software. The software, which is offered by a number of providers, delivers the specific analysis an organization needs to better their business.

The software is able to quickly and easily generate charts and graphs when conducting descriptive statistics, while at the same time conduct the more sophisticated computations that are required when conducting inferential statistics.

Among some of the more popular statistical analysis software services are IBM's SPSS, SAS, Revolution Analytics' R, Minitab and Stata.