TNPSC QUESTION PAPER 2016

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...

Methods of correlation

Correlation Methods Correlation and Covariance Matrices Fisher's z Transformation ( z r ) Pearson Product-Moment Correlation Coefficient (Pearson's r ) Spearman's Rank-Order Correlation Coefficient (Spearman's ρ) Kendall's Rank Correlation Coefficient (Kendall's τ) Correlation and Covariance Matrices You can generate a correlation or covariance matrix from numeric data columns, and have the choice of storing the computation results in an-autogenerated worksheet, or display the results in a table format whose values can be color coded. This method requires multiple numeric data columns whose values should be stored in a single worksheet. An example of a correlation matrix displayed as a color-coded table is shown below. Using Fisher's z Transformation ( z r ) This option is provided to allow transforming a skewed sampling distribution into a normalized format. The theoretical sampling distribution of the corr...

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...

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