tailieunhanh - Lập Trình C# all Chap "NUMERICAL RECIPES IN C" part 131

Tham khảo tài liệu 'lập trình c# all chap "numerical recipes in c" part 131', công nghệ thông tin phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 628 Chapter 14. Statistical Description of Data Stephens . 1970 Journal of the Royal Statistical Society ser. B vol. 32 pp. 115-122. 1 Anderson . and Darling . 1952 Annals ofMathematical Statistics vol. 23 pp. 193-212. 2 Darling . 1957 Annals of Mathematical Statistics vol. 28 pp. 823-838. 3 Michael . 1983 Biometrika vol. 70 no. 1 pp. 11-17. 4 Noe M. 1972 Annals of Mathematical Statistics vol. 43 pp. 58-64. 5 Kuiper . 1962 Proceedings ofthe Koninklijke Nederlandse Akademie van Wetenschappen ser. A. vol. 63 pp. 38-47. 6 Stephens . 1965 Biometrika vol. 52 pp. 309-321. 7 Fisher . Lewis T. and Embleton . 1987 Statistical Analysis of Spherical Data New York Cambridge University Press . 8 Contingency Table Analysis of Two Distributions In this section and the next two sections we deal with measures of association for two distributions. The situation is this Each data point has two or more different quantities associated with it and we want to know whether knowledge of one quantity gives us any demonstrable advantage in predicting the value of another quantity. In many cases one variable will be an independent or control variable and another will be a dependent or measured variable. Then we want to know if the latter variable is in fact dependent on or associated with the former variable. If it is we want to have some quantitative measure of the strength of the association. One often hears this loosely stated as the question of whether two variables are correlated or uncorrelated but we will reserve those terms for a particular kind of association linear or at least monotonic as discussed in and . Notice that as in previous sections the different concepts of significance and strength appear The association between two distributions may be very significant even if that association is weak if the quantity of data is large enough. It is useful to distinguish among some different kinds of variables with different categories forming a

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