Statistical Analysis

Look around you. Statistics are everywhere.

The field of statistics touches our lives in many ways. From the daily routines in our homes to the business of making the greatest cities run, the effects of statistics are everywhere.

Statistical Analysis Defined

What is statistical analysis? It’s the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics are applied every day, by data scientists and others – 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.

Look around you. From the tube of toothpaste in your bathroom to the planes flying overhead, you see hundreds of products and processes every day that have been improved through the use of statistics.

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More on statistical analysis

Statistics is so unique because it can go from health outcomes research to marketing analysis to the longevity of a light bulb. It’s a fun field because you really can do so many different things with it.

Besa Smith
President and Senior Scientist
Analydata

Statistical Computing

Traditional methods for statistical analysis – from sampling data to interpreting results – have been used by scientists for thousands of years. But today’s data volumes make statistics ever more valuable and powerful. Affordable storage, powerful computers and advanced algorithms have all led to an increased use of computational statistics.

Whether you are working with large data volumes or running multiple permutations of your calculations, statistical computing has become essential for today’s statistician. Popular statistical computing practices include:

Statistical programming – From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming is essential for making data-based decisions in every field.
Econometrics – Modeling, forecasting and simulating business processes for improved strategic and tactical planning. This method applies statistics to economics to forecast future trends.
Operations research – Identify the actions that will produce the best results – based on many possible options and outcomes. Scheduling, simulation, and related modeling processes are used to optimize business processes and management challenges.
Matrix programming – Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.
Statistical quality improvement – A mathematical approach to reviewing the quality and safety characteristics for all aspects of production.

Careers in Statistical Analysis

With everyone from The New York Times to Google’s Chief Economist Hal Varien proclaiming statistics to be the latest hot career field, who are we to argue? But why is there so much talk about careers in statistical analysis and data science? It could be the shortage of trained analytical thinkers. Or it could be the demand for managing the latest big data strains. Or, maybe it’s the excitement of applying mathematical concepts to make a difference in the world.

If you talk to statisticians about what first interested them in statistical analysis, you’ll hear a lot of stories about collecting baseball cards as a child. Or applying statistics to win more games of Axis and Allies. It is often these early passions that lead statisticians into the field. As adults, those passions can carry over into the workforce as a love of analysis and reasoning, where their passions are applied to everything from the influence of friends on purchase decisions to the study of endangered species around the world.

Learn more about the field of statistics and real-world statisticians:

Read the personal stories of data scientists solving real-world problems.
Learn about statistics and advanced analytics on the SAS Data Science blog.

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