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The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics. [1] [2] The theory covers approaches to statistical-decision problems and to statistical inference, and the actions and deductions that satisfy the basic principles stated for these different approaches.
In business, "statistics" is a widely used management-and decision support tool. It is particularly applied in financial management, marketing management, and production, services and operations management. [69] [70] Statistics is also heavily used in management accounting and auditing.
However, the terminology can be confusing, as the "classical" interpretation of probability aligns with Bayesian principles, while "classical" statistics follow the frequentist approach. Moreover, even within the term "frequentist," there are variations in interpretation, differing between philosophy and physics.
Physicists face the same situation in the kinetic theory of gases, where the system, while deterministic in principle, is so complex (with the number of molecules typically the order of magnitude of the Avogadro constant 6.02 × 10 23) that only a statistical description of its properties is feasible.
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.
As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of data. [1] Methods of probability theory also apply to descriptions of complex systems given only partial knowledge of their state, as in statistical mechanics or sequential estimation .
Pages in category "Statistical principles" The following 14 pages are in this category, out of 14 total. ... Statistics; Cookie statement; Mobile view ...
Statistical models are often used even when the data-generating process being modeled is deterministic. For instance, coin tossing is, in principle, a deterministic process; yet it is commonly modeled as stochastic (via a Bernoulli process). Choosing an appropriate statistical model to represent a given data-generating process is sometimes ...