Can You Describe The Origin Of Statistics,the Importance, And The Uses?


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uses of statistics are underlined

According to Wikipedia:
Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. Also with prediction and forecasting based on data. It is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities, government and business.
  Some scholars pinpoint the origin of statistics to 1662, with the publication of "Observations on the Bills of Mortality" by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and the natural and social sciences.    Because of its empirical roots and its applications, statistics is generally considered not to be a subfield of pure mathematics, but rather a distinct branch of applied mathematics. Its mathematical foundations were laid in the 17th century with the development of probability theory by Pascal and Fermat. Probability theory arose from the study of games of chance. The method of least squares was first described by Carl Friedrich Gauss around 1794. The use of modern computers has expedited large-scale statistical computation, and has also made possible new methods that are impractical to perform manually. 
Acccording to Webster'
Statistics is a branch of applied mathematics which includes the planning, summarizing, and interpreting of uncertain observations. Because the aim of statistics is to produce the "best" information from available data, some authors make statistics a branch of decision theory. As a model of randomness or ignorance, probability theory plays a critical role in the development of statistical theory.
We describe our knowledge (and ignorance) mathematically and attempt to learn more from whatever we can observe. This requires us to

1. Plan our observations to control their variability (experiment design),
2. Summarize a collection of observations to feature their commonality by suppressing details (descriptive statistics), and
3. Reach consensus about what the observations tell us about the world we observe (statistical inference).

In some forms of descriptive statistics, notably data mining, the second and third of these steps become so prominent that the first step (planning) appears to become less important. In these disciplines, data often are collected outside the control of the person doing the analysis, and the result of the analysis may be more an operational model than a consensus report about the world.
The probability of an event is often defined as a number between one and zero rather than a percentage.
Some sciences use applied statistics so extensively that they have specialized terminology. These disciplines include:
Business statistics
Economic statistics
Engineering statistics
Population statistics
Psychological statistics
Social statistics (for all the social sciences)
Process analysis and Chemometrics (for analysis of data from analytical chemistry and chemical engineering)

Statistics form a key basis tool in business and manufacturing as well. It is used to understand measurement systems variability, control processes (as in "statistical process control" or SPC), for summarizing data, and to make data-driven decisions. In these roles it is a key tool, and perhaps the only reliable tool.

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