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Why is statistics needed in data mining?
Statistics is the science of learning from data. It contains everything from planning for the set of records and subsequent data administration to end-of-the-line activities including drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with the most essential of person required: the need to discover out more about the globe and how it works in face of innovation and uncertainty.
Information is the communication of knowledge. Data are referred to be crude data and not knowledge by themselves. The sequence from data to knowledge is as follows: from data to information (data develop into information when they develop into relevant to the decision problem); from information to facts (information becomes facts when the data can support it) and finally, from facts to knowledge (facts become knowledge when they are used in the successful competition of the decision process).
Statistics arose from the need to place knowledge on a systematic evidence base. This needed a study of the laws of probability, the development of computing of data properties and relationships, etc.
Statistics defines the analysis and presentation of numeric records, which is the essential element of all data mining algorithm. It supports tools and analytics methods to deal with a huge amount of data. Statistics incorporates planning, designing, gathering information, analyzing, and reporting research findings. Because these statistics are not only defined to mathematics, but a business analyst also uses statistics to solve business issues.
Inferential statistics is used for a sample to estimate the values of a population’s parameters. It can carry out hypothesis tests to see if two datasets are similar or disparate. It is used to conduct linear- or multiple-regression analysis to explain causation.
Hypothesis testing can numerically compare two datasets. For instance, it can feel (hypothesize) that this sales volume is similar, or better than that of the main competitor. It can use hypothesis testing to mathematically confirm or reject this assumption.
Correlation analysis is a simple tool to isolate the variables of interest from several random variables, often observed in huge datasets, to see which business variables significantly affect the desired business outcome.
Several statistics can be used to prepare charts for quality control, including Shewhart charts and cusum charts (both of which display group summary statistics). These statistics contain the mean, standard deviation, range, count, moving average, moving standard deviation, and moving range.
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