A detail about Father of Statistics

Born	17 February 1890 East Finchley, London, England
Died	29 July 1962 (aged 72) Adelaide, South Australia
Residence	England and Australia
Nationality	British
Fields	Statistics, Genetics, andEvolutionary biology
Institutions	Rothamsted Experimental Station University College London Cambridge University University of Adelaide, and CSIRO
Alma mater	University of Cambridge
Academic advisors	Sir James Jeans and F.J.M. Stratton
Doctoral students	C.R. Rao, D. J. Finney, and Walter Bodmer
Known for	Fisher's fundamental theorem Maximum likelihood Fisher information Analysis of variance The Fisher–Kolmogorov equation Fisher's geometric model Coining the term "null hypothesis" Fiducial inference Fisher's exact test Fisher's principle Fisherian runaway The F-distribution
Influences	Leonard Darwin
Influenced	Joseph Oscar Irwin A. W. F. Edwards Georg Rasch W. D. Hamilton Oscar Kempthorne, Richard Dawkins
Notable awards	Royal Medal (1938) Guy Medal in Gold (1946) Copley Medal (1955) Linnean Society of London'sDarwin–Wallace Medal (1958).
Notes: He was the father-in-law of George E. P. Box.

Sir Ronald Aylmer Fisher FRS (17 February 1890 – 29 July 1962) was an English statistician, evolutionary biologist, geneticist, andeugenicist. Fisher is known as one of the chief architects of the neo-Darwinian synthesis, and for his important contributions to statistics, including the Analysis of Variance (ANOVA), method of maximum likelihood, fiducial inference, and the derivation of various sampling distributions. Anders Hald called him "a genius who almost single-handedly created the foundations for modern statistical science",^[1] whileRichard Dawkins named him "the greatest biologist since Darwin".^[2]

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Runs Test for Detecting Non-randomness

Runs Test for Detecting Non-randomness Purpose: Detect Non-Randomness The runs test ( Bradley, 1968 ) can be used to decide if a data set is from a random process. A run is defined as a series of increasing values or a series of decreasing values. The number of increasing, or decreasing, values is the length of the run. In a random data set, the probability that the ( I +1)th value is larger or smaller than the I th value follows a binomial distribution , which forms the basis of the runs test. Typical Analysis and Test Statistics The first step in the runs test is to count the number of runs in the data sequence. There are several ways to define runs in the literature, however, in all cases the formulation must produce a dichotomous sequence of values. For example, a series of 20 coin tosses might produce the f...

The most femiliar statisticians

Gertrude Cox : Gertrude Mary Cox (of Experimental Statistics at North Carolina State University. She was later appointed director of both the Institute of Statistics of 1900 - 1978) was an influential American statistician and founder of the department the Consolidated University of North Carolina and the Statistics Research Division of North Carolina State University. Her most important and influential research dealt with experimental design; she wrote an important book on the subject with W. G. Cochran. In 1949 Cox became the first female elected into the International Statistical Institute and in 1956 she was president of the American Statistical Association. From 1931 to 1933 Cox undertook graduate studies in statistics at the University of California at Berkeley , then returned to Iowa State College as assistant in the Statistical Laboratory. Here she worked on the design of experiments . In 1939 she was appointed assistant professor of statisti...

Lognormal distribution

Lognormal Distribution Probability Density Function A variable X is lognormally distributed if Y = LN(X) is normally distributed with "LN" denoting the natural logarithm. The general formula for the probability density function of the lognormal distribution is where is the shape parameter , is the location parameter and m is the scale parameter . The case where = 0 and m = 1 is called the standard lognormal distribution . The case where equals zero is called the 2-parameter lognormal distribution. The equation for the standard lognormal distribution is Since the general form of probability functions can be expressed in terms of the standard distribution , all subsequent formulas in this section are given for the standard form of the function. The following is the plot of the lognormal probability density function for four values of . There are several commo...

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