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Mathematical statistics is the application of probability theory and other mathematical concepts to statistics, as opposed to techniques for collecting statistical data. [1] Specific mathematical techniques that are commonly used in statistics include mathematical analysis , linear algebra , stochastic analysis , differential equations , and ...
It includes the founders of statistics and others. It includes some 17th- and 18th-century mathematicians and polymaths whose work is regarded as influential in shaping the later discipline of statistics. Also included are various actuaries, economists, and demographers known for providing leadership in applying statistics to their fields.
Statistics is the theory and application of mathematics to the scientific method including hypothesis generation, experimental design, sampling, data collection, data summarization, estimation, prediction and inference from those results to the population from which the experimental sample was drawn.
Mathematical statistics is the application of mathematics to statistics. Mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory. [1] [7] All statistical analyses make use of at least some mathematics, and mathematical statistics ...
The modern mathematical theory of probability has its roots in attempts to analyze games of chance by Gerolamo Cardano in the sixteenth century, and by Pierre de Fermat and Blaise Pascal in the seventeenth century (for example the "problem of points"). [3] Christiaan Huygens published a book on the subject in 1657. [4]
The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis : an example of such a ...
Harold Jeffreys (1891–1989) - one of the giants within Bayesian statistics school; Richard Jeffrey (1926–2002) - best known for the philosophy of radical probabilism and Jeffrey conditioning; Karamat Ali Karamat (1936–2022) Terence Tao (born 1975) Richard M. Dudley (1938–2020) William Timothy Gowers (born 1963) Bálint Tóth (born 1955)
A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes ("heads" and "tails") are both equally probable; the probability of "heads" equals the probability of "tails"; and since no other outcomes are possible, the probability of either "heads" or "tails" is 1/2 (which could also be written as 0.5 or 50%).