Учебник по промышленной статистике


Список использованной литературы - часть 49


Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrica Bulletin, 1, 80-83.

Wilcoxon, F. (1947). Probability tables for individual comparisons by ranking methods. Biometrics, 3, 119-122.

Wilcoxon, F. (1949). Some rapid approximate statistical procedures. Stamford, CT: American Cyanamid Co.

Wilde, D. J., & Beightler, C. S. (1967). Foundations of optimization. Englewood Cliffs, NJ: Prentice-Hall.

Wilks, S. S. (1943). Mathematical Statistics. Princeton, NJ: Princeton University Press.

Wilks, S. S. (1946). Mathematical statistics. Princeton, NJ: Princeton University Press.

Williams, W. T., Lance, G. N., Dale, M. B., & Clifford, H. T. (1971). Controversy concerning the criteria for taxonometric strategies. Computer Journal, 14, 162.

Wilson, G. A., & Martin, S. A. (1983). An empirical comparison of two methods of testing the significance of a correlation matrix. Educational and Psychological Measurement, 43, 11-14.

Winer, B. J. (1962). Statistical principles in experimental design. New York: McGraw-Hill.

Winer, B. J. (1971). Statistical principles in experimental design (2nd ed.). New York: McGraw-Hill.

Winer, B. J. (1971). Statistical principles in experimental design (2nd ed.). New York: McGraw Hill.

Wolfowitz, J. (1942). Additive partition functions and a class of statistical hypotheses. Annals of Mathematical Statistics, 13, 247-279.

Wolynetz, M. S. (1979a). Maximum likelihood estimation from confined and censored normal data. Applied Statistics, 28, 185-195.

Wolynetz, M. S. (1979b). Maximum likelihood estimation in a linear model from confined and censored normal data. Applied Statistics, 28, 195-206.

Wonnacott, R. J., & Wonnacot, T. H. (1970). Econometrics. New York: Wiley.

Woodward, J. A., Bonett, D. G., & Brecht, M. L. (1990). Introduction to linear models and experimental design. New York: Harcourt, Brace, Jovanovich.

Woodward, J. A., & Overall, J. E. (1975). Multivariate analysis of variance by multiple regression methods. Psychological Bulletin, 82, 21-32.




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