Abstract
The exploration of experimental data and the reliability of the statistical inference based on these data depend heavily on the selection of the mathematical model and on the design of the data collection method.
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Bibliographical notes
S.B. Vardeman, Statistics for Engineering Problem Solving PWS Publishing, Boston, 1994
D.C. Montgomery and G.C. Runger, Applied Statistics and Probability for Engineers, John Wiley, Inc., New York, 1994.
M.J. Schervish, Theory of Statistics, Springer-Verlag, New York, 1995
S.R. Searle, Linear Models, J. Wiley, 1971
E.H. Lehman, Theory of Point Estimation, John Wiley, New York, 1981
E.H. Lehman, Testing Statistical Hypotheses, John Wiley, New York, 1959
M. Hollander and G.A. Wolfe, Nonparametric Statistical Analysis, John Wiley, New York, 1973.
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© 1998 Springer Science+Business Media New York
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Denker, M., Woyczyński, W.A., Ycart, B. (1998). General Principles of Statistical Analysis. In: Introductory Statistics and Random Phenomena. Statistics for Industry and Technology. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-2028-2_7
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DOI: https://doi.org/10.1007/978-1-4612-2028-2_7
Publisher Name: Birkhäuser, Boston, MA
Print ISBN: 978-1-4612-7388-2
Online ISBN: 978-1-4612-2028-2
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