TAILIEUCHUNG - Lập Trình C# all Chap "NUMERICAL RECIPES IN C" part 128

Tham khảo tài liệu 'lập trình c# all chap "numerical recipes in c" part 128', công nghệ thông tin phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 610 Chapter 14. Statistical Description of Data In the other category model-dependent statistics we lump the whole subject of fitting data to a theory parameter estimation least-squares fits and so on. Those subjects are introduced in Chapter 15. Section deals with so-called measures of central tendency the moments of a distribution the median and mode. In we learn to test whether different data sets are drawn from distributions with different values of these measures of central tendency. This leads naturally in to the more general question of whether two distributions can be shown to be significantly different. In we deal with measures of association for two distributions. We want to determine whether two variables are correlated or dependent on one another. If they are we want to characterize the degree of correlation in some simple ways. The distinction between parametric and nonparametric rank methods is emphasized. Section introduces the concept of data smoothing and discusses the particular case of Savitzky-Golay smoothing filters. This chapter draws mathematically on the material on special functions that was presented in Chapter 6 especially . You may wish at this point to review those sections. CITED REFERENCES AND FURTHER READING Bevington . 1969 Data Reduction and Error Analysis for the Physical Sciences New York McGraw-Hill . Stuart A. and Ord . 1987 Kendall s Advanced Theory of Statistics 5th ed. London Griffin and Co. previous eds. published as Kendall M. and Stuart A. The Advanced Theory of Statistics Norusis . 1982 SPSS Introductory Guide Basic Statistics and Operations and 1985 SPSS-X Advanced Statistics Guide New York McGraw-Hill . Dunn . and Clark . 1974 Applied Statistics Analysis ofVariance and Regression New York Wiley . Moments of a Distribution Mean Variance Skewness and So Forth When a set of values has a sufficiently strong central tendency that is a tendency to cluster around some .

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