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1

McCullough, B. D. "Mathematical Statistics With Mathematica". Journal of the American Statistical Association 97, n. 460 (dicembre 2002): 1202–3. http://dx.doi.org/10.1198/jasa.2002.s230.

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2

Attwood, G. B., John E. Freund e Ronald E. Walpole. "Mathematical Statistics". Mathematical Gazette 72, n. 459 (marzo 1988): 76. http://dx.doi.org/10.2307/3618025.

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3

Lindley, D. V., A. A. Borovkov e A. Moullagaliev. "Mathematical Statistics". Mathematical Gazette 83, n. 497 (luglio 1999): 374. http://dx.doi.org/10.2307/3619119.

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4

Strawderman, William E., e Jun Shao. "Mathematical Statistics". Journal of the American Statistical Association 95, n. 449 (marzo 2000): 329. http://dx.doi.org/10.2307/2669561.

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5

Jammalamadaka, Sreenivasa Rao, e A. A. Borovkov. "Mathematical Statistics". Journal of the American Statistical Association 95, n. 449 (marzo 2000): 330. http://dx.doi.org/10.2307/2669562.

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6

Pillers Dobler, Carolyn. "Mathematical Statistics". American Statistician 56, n. 4 (novembre 2002): 332. http://dx.doi.org/10.1198/tas.2002.s204.

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7

Geyer, C. "Mathematical Statistics". Journal of the American Statistical Association 96, n. 454 (giugno 2001): 779. http://dx.doi.org/10.1198/jasa.2001.s397.

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8

Massam, Hélène. "Mathematical Statistics". Journal of Statistical Planning and Inference 92, n. 1-2 (gennaio 2001): 305–6. http://dx.doi.org/10.1016/s0378-3758(00)00154-3.

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9

KRASNOZHON, O. B., e V. V. MATSIUK. "КОМП’ЮТЕРНО-ОРІЄНТОВАНІ ЕЛЕМЕНТИ НАВЧАННЯ МАТЕМАТИЧНИХ ДИСЦИПЛІН МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ". Scientific papers of Berdiansk State Pedagogical University Series Pedagogical sciences 1, n. 2 (4 ottobre 2021): 255–62. http://dx.doi.org/10.31494/2412-9208-2021-1-2-255-262.

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Abstract (sommario):
The article is devoted to the issues of constructing effective computer-oriented components of the methodological system of teaching the disciplines «Linear Algebra» and «Probability Theory with Elements of Mathematical Statistics» provided for in the educational and professional program «Secondary Education (Mathematics)» of the first level of higher education in the specialty 014 Secondary Education (Mathematics). The article analyzes the methodological aspects of the effective organization of computations when finding the angle between a given vector and a nonzero subspace of Euclidean space, as well as using the least squares method for processing experimental data. The theoretical and practical information known to students-mathematicians from the corresponding sections of these academic disciplines is briefly presented. Analyzed educational, methodological and scientific literature used in teaching linear algebra and probability theory with elements of mathematical statistics; the expediency of using computer-oriented elements of teaching mathematical disciplines of future mathematics teachers has been substantiated. The authors proposed the use of computer-oriented learning elements in the processing of the content of disciplines and the development of test tasks of different levels of complexity in linear algebra and probability theory with elements of mathematical statistics in order to objectively assess the level of students' knowledge and timely correct individual educational trajectories. The article provides examples of the application of computer-oriented elements of teaching linear algebra and probability theory with elements of mathematical statistics, and also analyzes the methodological features of the organization of calculations in the software mathematical environment Mathcad. The methodological and practical materials presented in the article can be useful for students to organize and activate independent scientific and pedagogical activities, teachers of secondary educational institutions, heads of optional and circle work of students, teachers of linear algebra and probability theory courses with elements of mathematical statistics of pedagogical higher educational institutions. Key words: methods of teaching mathematics, computer-oriented elements of teaching mathematics, linear algebra, probability theory, mathematical statistics, Euclidean space, non-zero subspace of Euclidean space, least squares method.
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10

Madsen, Richard, Edward J. Dudewicz e Satya N. Mishra. "Modern Mathematical Statistics." Journal of the American Statistical Association 84, n. 405 (marzo 1989): 335. http://dx.doi.org/10.2307/2289889.

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11

McKean, Joseph W., e Edward B. Manoukian. "Mathematical Nonparametric Statistics." Journal of the American Statistical Association 83, n. 404 (dicembre 1988): 1217. http://dx.doi.org/10.2307/2290166.

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12

Rasch, D., E. J. Dudewicz e S. N. Mishra. "Modern Mathematical Statistics." Biometrics 46, n. 1 (marzo 1990): 291. http://dx.doi.org/10.2307/2531671.

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13

Ziegel, Eric. "Modern Mathematical Statistics". Technometrics 31, n. 3 (agosto 1989): 391. http://dx.doi.org/10.1080/00401706.1989.10488574.

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14

Leitch, R. D., E. J. Dudewicz e S. N. Mishra. "Modern Mathematical Statistics." Statistician 41, n. 2 (1992): 257. http://dx.doi.org/10.2307/2348274.

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15

Watson, Rex, H. T. Nguyen e G. S. Rogers. "Fundamentals of Mathematical Statistics". Mathematical Gazette 74, n. 469 (ottobre 1990): 319. http://dx.doi.org/10.2307/3619864.

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16

Graybill, Franklin A., H. T. Nguyen e G. S. Rogers. "Fundamentals of Mathematical Statistics". Journal of the American Statistical Association 86, n. 413 (marzo 1991): 247. http://dx.doi.org/10.2307/2289743.

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17

AA, Robert V. Hogg e Allen T. Craig. "Introduction to Mathematical Statistics." Journal of the American Statistical Association 91, n. 433 (marzo 1996): 441. http://dx.doi.org/10.2307/2291439.

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18

Loukas, S., W. Mendenhall, D. D. Wackerly e R. L. Scheaffer. "Mathematical Statistics with Applications." Biometrics 48, n. 3 (settembre 1992): 977. http://dx.doi.org/10.2307/2532372.

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19

Williamson, Patricia Pepple. "Mathematical Statistics With Applications". Journal of the American Statistical Association 101, n. 473 (marzo 2006): 394–95. http://dx.doi.org/10.1198/jasa.2006.s73.

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20

Sylvan, Dana. "Introduction to Mathematical Statistics". Journal of Biopharmaceutical Statistics 23, n. 3 (23 aprile 2013): 716–17. http://dx.doi.org/10.1080/10543406.2013.756334.

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21

O'Dell, Jerry W., e Sylvia Von Kluge. "Mathematical Difficulties in Statistics". Psychological Reports 72, n. 2 (aprile 1993): 495–98. http://dx.doi.org/10.2466/pr0.1993.72.2.495.

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Abstract (sommario):
79 and 120 students in classes in Psychological Statistics were tested in 1976 and 1991, respectively, with a mathematical readiness test devised by Brown in 1933. A general decline in mathematical ability as measured by the test was found, especially with items involving mathematical reasoning and algebra. Over-all mean scores dropped significantly from 18.8 to 16.6.
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22

Meek, Gary E. "Mathematical Statistics With Applications". Technometrics 29, n. 3 (agosto 1987): 384–85. http://dx.doi.org/10.1080/00401706.1987.10488256.

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23

Rota, Gian-Carlo. "Mathematical basis of statistics". Advances in Mathematics 60, n. 1 (aprile 1986): 123. http://dx.doi.org/10.1016/0001-8708(86)90007-1.

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24

Young, Karen, W. Mendenhall, D. D. Wackerley e R. L. Scheaffer. "Mathematical Statistics with Applications." Applied Statistics 41, n. 2 (1992): 433. http://dx.doi.org/10.2307/2347577.

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25

Bondar, James V., Helmut Strasser e Walter de Gruyter. "Mathematical Theory of Statistics." Journal of the American Statistical Association 82, n. 399 (settembre 1987): 950. http://dx.doi.org/10.2307/2288818.

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26

Solomon, Herbert, R. E. Odeh, J. M. Davenport e N. S. Pearson. "Selected Tables in Mathematical Statistics." Mathematics of Computation 48, n. 178 (aprile 1987): 848. http://dx.doi.org/10.2307/2007858.

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27

Middleton, D., e John A. Rice. "Mathematical Statistics and Data Analysis". Mathematical Gazette 72, n. 462 (dicembre 1988): 330. http://dx.doi.org/10.2307/3619963.

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28

Ziegel, Eric R., e John Rice. "Mathematical Statistics and Data Analysis". Technometrics 37, n. 1 (febbraio 1995): 127. http://dx.doi.org/10.2307/1269179.

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29

Leonard, Tom, e John A. Rice. "Mathematical Statistics and Data Analysis." Journal of the American Statistical Association 84, n. 406 (giugno 1989): 625. http://dx.doi.org/10.2307/2289973.

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30

AA e John A. Rice. "Mathematical Statistics and Data Analysis." Journal of the American Statistical Association 90, n. 429 (marzo 1995): 398. http://dx.doi.org/10.2307/2291188.

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31

Jammalamadaka, Sreenivasa Rao, e George R. Terrell. "Mathematical Statistics: A Unified Introduction". Journal of the American Statistical Association 95, n. 449 (marzo 2000): 331. http://dx.doi.org/10.2307/2669563.

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32

Rosalsky, Andrew, I. A. Ibragimov e A. Yu Zaitsev. "Probability Theory and Mathematical Statistics". Journal of the American Statistical Association 94, n. 448 (dicembre 1999): 1387. http://dx.doi.org/10.2307/2669958.

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33

Macky, David W., e George C. Roussas. "A Course in Mathematical Statistics". American Statistician 53, n. 1 (febbraio 1999): 87. http://dx.doi.org/10.2307/2685661.

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34

Eileen Magnello, M. "Victorian vital and mathematical statistics". BSHM Bulletin: Journal of the British Society for the History of Mathematics 21, n. 3 (novembre 2006): 219–29. http://dx.doi.org/10.1080/17498430600964508.

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35

Stamey, James. "Modern Mathematical Statistics with Applications". American Statistician 62, n. 4 (novembre 2008): 358. http://dx.doi.org/10.1198/tas.2008.s266.

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36

Begg, Andy. "Statistics and the Mathematical Processes". Teaching Statistics 17, n. 2 (giugno 1995): 40–43. http://dx.doi.org/10.1111/j.1467-9639.1995.tb00862.x.

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37

May, E. Lee. "An experiment with mathematical statistics". International Journal of Mathematical Education in Science and Technology 21, n. 4 (luglio 1990): 559–66. http://dx.doi.org/10.1080/0020739900210407.

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38

Larsen, Pia Veldt. "Approximation Theorems of Mathematical Statistics". Journal of the Royal Statistical Society: Series D (The Statistician) 52, n. 2 (luglio 2003): 247–48. http://dx.doi.org/10.1111/1467-9884.t01-8-00356.

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39

Marron, J. S. "Effective writing in mathematical statistics". Statistica Neerlandica 53, n. 1 (marzo 1999): 68–75. http://dx.doi.org/10.1111/1467-9574.00098.

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40

Rabinowitz, Larry. "Mathematical Statistics and Data Analysis". Technometrics 31, n. 3 (agosto 1989): 390–91. http://dx.doi.org/10.1080/00401706.1989.10488573.

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41

Healy, John. "Selected Tables in Mathematical Statistics". Technometrics 31, n. 3 (agosto 1989): 397. http://dx.doi.org/10.1080/00401706.1989.10488584.

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42

George, Larry. "Mathematical Statistics, a Unified Introduction". Technometrics 42, n. 3 (agosto 2000): 319–21. http://dx.doi.org/10.1080/00401706.2000.10486071.

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43

Arnold, Barry C., B. K. Shah e R. E. Odeh. "Selected Tables in Mathematical Statistics." Journal of the American Statistical Association 82, n. 399 (settembre 1987): 951. http://dx.doi.org/10.2307/2288819.

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44

Rukhin, Andrew L. "Kolmogorov's Contributions to Mathematical Statistics". Annals of Statistics 18, n. 3 (settembre 1990): 1011–16. http://dx.doi.org/10.1214/aos/1176347737.

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45

Notz, William I., W. J. Kennedy, R. E. Odeh e J. M. Davenport. "Selected Tables in Mathematical Statistics." Mathematics of Computation 47, n. 176 (ottobre 1986): 759. http://dx.doi.org/10.2307/2008197.

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46

Bailey, B. J. R., S. M. Lewis, W. J. Kennedy e R. E. Odeh. "Selected Tables in Mathematical Statistics." Journal of the Royal Statistical Society. Series A (General) 150, n. 2 (1987): 171. http://dx.doi.org/10.2307/2981640.

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47

Stephens, H. J., R. E. Odeh e J. M. Davenport. "Selected Tables in Mathematical Statistics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 152, n. 2 (1989): 271. http://dx.doi.org/10.2307/2982943.

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48

Rivadulla, Andrés. "Mathematical statistics and metastatistical analysis". Erkenntnis 34, n. 2 (marzo 1991): 211–36. http://dx.doi.org/10.1007/bf00385721.

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49

Chellappa, Rama. "Mathematical statistics and computer vision". Image and Vision Computing 30, n. 8 (agosto 2012): 467–68. http://dx.doi.org/10.1016/j.imavis.2012.03.008.

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50

Mahler, Ronald. "“Statistics 103” for Multitarget Tracking". Sensors 19, n. 1 (8 gennaio 2019): 202. http://dx.doi.org/10.3390/s19010202.

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Abstract (sommario):
The finite-set statistics (FISST) foundational approach to multitarget tracking and information fusion was introduced in the mid-1990s and extended in 2001. FISST was devised to be as “engineering-friendly” as possible by avoiding avoidable mathematical abstraction and complexity—and, especially, by avoiding measure theory and measure-theoretic point process (p.p.) theory. Recently, however, an allegedly more general theoretical foundation for multitarget tracking has been proposed. In it, the constituent components of FISST have been systematically replaced by mathematically more complicated concepts—and, especially, by the very measure theory and measure-theoretic p.p.’s that FISST eschews. It is shown that this proposed alternative is actually a mathematical paraphrase of part of FISST that does not correctly address the technical idiosyncrasies of the multitarget tracking application.
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