Statistical analysis can be defined as the group of methods used to process huge data and report overall patterns. This analysis is mainly used when noisy data is obtained. This type of analysis offers ways to report objectively how rare an event is based on the historical data. Usually servers uses this analysis to analyse the huge amounts of data generates everyday by the stock market. We usually choose this analysis to more conventional forms of technical analysis because statistical analysis makes use of every small detail. This type of analysis looks at more than data and mostly needs a computer for analytical purposes.
Some of the conventional methods of technical analysis were candlesticks, points and figure charts which were particularly formulated for people who were assessing data by means of hand.
Statistical analysis is the science of gathering, researching and projecting huge quantities of data to understand the hidden trends and patterns. This can be applied to all types of industries from research, government and manufacturing units. For example:
Initially in the olden days, traditional methods were used for statistical analysis right from data sampling to results interpretation. On the contrary, in today's world data volumes makes statistics even more precious and authoritative. Cheap storage, highly powered computer systems and advanced technology have all resulted in the high usage of computational statistics.
Irrespective of whether one works with huge data volumes or by processing multiple permutation, statistical computing plays a vital role for a statistician in today's world. Some of the famous computing practices consist of statistical programming, econometrics, operational research, programming of matrices, statistical data visualization, quality enhancements in terms of statistical analysis and finally high-performance.
Operations research usually recognises actions that will generate the best results based on various options and results. Certain processes such as scheduling, simulation and modeling processes linked to this analysis are used to standardize business processes and other management hurdles.
Matrix programming makes use of highly powerful techniques for execution of statistical methods and other exploratory data analysis using various row operation algorithms.
On the other hand, statistical quality improvement is a mathematical method to review quality and safety in all contexts of production and statistical visualization is a rapid and interactive statistical analysis that has the capacity to view the data in a visual interface. This is beneficial because this can be used to understand the data given and build models based on it.