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.

• Predictive Analytics: If you want to make predictions about future events, predictive analysis is what you need. This analysis is based on current and historical facts. Predictive analytics uses statistical algorithms and machine learning techniques to define the likelihood of future results, behavior, and trends based on both new and historical data. Marketing, financial services, online services providers, and insurance companies are among the main users of predictive analytics. More and more businesses are starting implementing predictive analytics to increase competitive advantage and to minimize the risk associated with unpredictable future. Predictive analytics can use a variety of techniques such as data mining, modeling, artificial intelligence, machine learning and etc. to make important predictions about the future.
• Prescriptive Analytics:Prescriptive analytics is a study which examines data to answer the question “What should be done?” It is a common area of business analysis dedicated to identifying the best movie or action for a specific situation. Prescriptive analytics aim to find the optimal recommendations for a decision making process. It is all about providing advice. Prescriptive analytics is related to descriptive and predictive analytics. While descriptive analytics describe what has happened and predictive analytics helps to predict what might happen, prescriptive statistics aims to find the best options among available choices. Prescriptive analytics uses techniques such as simulation, graph analysis, business rules, algorithms, complex event processing, recommendation engines, and machine learning.
• Causal Analysis: When you want to understand and identify the reasons why things are as they are, causal analysis comes to help. This type of analysis answer the question “Why?” The business world is full of events that lead to failure. The causal seeks to identify the reasons why? It is better to find causes and to treat them instead of treating symptoms. Causal analysis searches for the root cause – the basic reason why something happens. Causal analysis is a common practice in industries that address major disasters. However, it is becoming more popular in the business, especially in IT field. For example, the causal analysis is a common practice in quality assurance in the software industry.The goals of casual analysis:
  • To identify key problem areas.
  • To investigate and determine the root cause.
  • To understand what happens to given variable if you change another.
• Exploratory Data Analysis (EDA) Exploratory data analysis (EDA) is a complement to inferential statistics. It is used mostly by data scientists. EDA is an analysis approach that focuses on identifying general patterns in the data and to find previously unknown relationships. The purpose of exploratory data analysis is:
  • Check mistakes or missing data.
  • Discover new connections.
  • Collect maximum insight into the data set.
  • Check assumptions and hypothesis.

EDA alone should not be used for generalizing or predicting. EDA is used for taking a bird’s eye view of the data and trying to make some feeling or sense of it. Commonly, it is the first step in data analysis, performed before other formal statistical techniques.

• Mechanistic Analysis: Mechanistic Analysis is a not common type of statistical analysis. However it worth mentioning here because, in some industries such as big data analysis, it has an important role. The mechanistic analysis is about understanding the exact changes in given variables that lead to changes in other variables. However, mechanistic does not consider external influences. The assumption is that a given system is affected by the interaction of its own components. It is useful on those systems for which there are very clear definitions. Biological science, for example, can make use of.

source: Intellspot

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.