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Journal articles on the topic 'Statistical'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 July 2025

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1

Vinciotti, Veronica, and Ernst C. Wit. "Statistica Neerlandica special issue on Statistical Network Science." Statistica Neerlandica 74, no. 3 (July 15, 2020): 220–21. http://dx.doi.org/10.1111/stan.12212.

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2

LAMB, CHRISTOPHER R. "STATISTICAL BRIEFING: STATISTICAL POWER." Veterinary Radiology & Ultrasound 50, no. 3 (May 2009): 336. http://dx.doi.org/10.1111/j.1740-8261.2009.01546.x.

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3

Çakan, Celal, and Bilal Altay. "Statistically boundedness and statistical core of double sequences." Journal of Mathematical Analysis and Applications 317, no. 2 (May 2006): 690–97. http://dx.doi.org/10.1016/j.jmaa.2005.06.006.

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4

Nuray, Fatih. "Lacunary weak statistical convergence." Mathematica Bohemica 136, no. 3 (2011): 259–68. http://dx.doi.org/10.21136/mb.2011.141648.

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5

Begum, Hasina. "Statistical Analysis in GWAS." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1072–78. http://dx.doi.org/10.21275/sr231215085226.

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6

International Monetary Fund. "Algeria: Statistical Appendix: Statistical Appendix." IMF Staff Country Reports 13, no. 49 (2013): 1. http://dx.doi.org/10.5089/9781475535648.002.

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7

Hahn, Gerald J., Necip Doganaksoy, and William Q. Meeker. "Statistical intervals, not statistical significance." Significance 16, no. 4 (July 24, 2019): 20–22. http://dx.doi.org/10.1111/j.1740-9713.2019.01298.x.

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8

Lê, Hông Vân. "Statistical manifolds are statistical models." Journal of Geometry 84, no. 1-2 (March 2006): 83–93. http://dx.doi.org/10.1007/s00022-005-0030-0.

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9

HU, DIHE. "I.I.D. STATISTICAL CONTRACTION OPERATORS AND STATISTICALLY SELF-SIMILAR SETS." Chinese Annals of Mathematics 23, no. 04 (October 2002): 461–68. http://dx.doi.org/10.1142/s0252959902000420.

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10

Htoon, Hla Myint, Yiong Huak Chan, and John Carson Allen. "Behind Every Familiar Statistical Test is a Famous Statistician." Proceedings of Singapore Healthcare 21, no. 3 (September 2012): 217–19. http://dx.doi.org/10.1177/201010581202100312.

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11

Iwasaki, Atsushi, Yoshinobu Shimamura, and Akira Todoroki. "OS17-3-6 Optimization of the statistical model for the statistical damage diagnostic method." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS17–3–6——_OS17–3–6—. http://dx.doi.org/10.1299/jsmeatem.2007.6._os17-3-6-.

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12

Mucha, Boris, Patrícia Brestovanská, and Tomáš Peráček. "Audit Sampling – statistical vs. non-statistical?" Journal of Eastern Europe Research in Business and Economics 2018 (November 28, 2018): 1–10. http://dx.doi.org/10.5171/2018.136905.

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13

Berry, Geoffrey. "Statistical guide‐lines and statistical guidance." Medical Journal of Australia 146, no. 8 (April 1987): 408–9. http://dx.doi.org/10.5694/j.1326-5377.1987.tb120330.x.

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14

Garreau, G. A., Myles Holland, and Frank Proschan. "The Statistical Exorcist: Dispelling Statistical Anxiety." Mathematical Gazette 70, no. 452 (June 1986): 160. http://dx.doi.org/10.2307/3615795.

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15

Taplin, Ross H. "Teaching Statistical Consulting Before Statistical Methodology." Australian New Zealand Journal of Statistics 45, no. 2 (June 2003): 141–52. http://dx.doi.org/10.1111/1467-842x.00270.

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16

Guo, Guangbao. "Parallel Statistical Computing for Statistical Inference." Journal of Statistical Theory and Practice 6, no. 3 (September 2012): 536–65. http://dx.doi.org/10.1080/15598608.2012.695705.

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17

Mascha, Edward J., and Thomas R. Vetter. "The Statistical Checklist and Statistical Review." Anesthesia & Analgesia 124, no. 3 (March 2017): 719–21. http://dx.doi.org/10.1213/ane.0000000000001863.

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18

Lubinsky, David J. "Integrating statistical theory with statistical databases." Annals of Mathematics and Artificial Intelligence 2, no. 1-4 (March 1990): 245–59. http://dx.doi.org/10.1007/bf01531010.

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19

Kucukaslan, E. Kaya, and R. Wagner. "On statistical convergence and statistical monotonicity." Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio computatorica, no. 39 (2013): 257–70. https://doi.org/10.71352/ac.39.257.

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20

Lasota, Andrzej, and James A. Yorke. "Statistical periodicity of deterministic systems." Časopis pro pěstování matematiky 111, no. 1 (1986): 1–13. http://dx.doi.org/10.21136/cpm.1986.118256.

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21

Kaur, Baljinder, and Neena Garg. "Statistical Optimization Of Ferulic Acid." Indian Journal of Applied Research 1, no. 4 (October 1, 2011): 1–6. http://dx.doi.org/10.15373/2249555x/jan2012/1.

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22

N Prasad, S., and Ajay Kumar Diwakar. "Statistical Analysis of Cancer Data." International Journal of Science and Research (IJSR) 11, no. 8 (August 5, 2022): 1153–54. http://dx.doi.org/10.21275/sr22819190317.

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23

Kaur, Gurinder. "Research Methodology and Statistical Analysis." International Journal of Science and Research (IJSR) 13, no. 7 (July 5, 2024): 586–89. http://dx.doi.org/10.21275/sr24710133153.

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24

HRYTSUN, Viktoriia. "STATISTICAL SECURITY OF ECONOMIC MANAGEMENT." Herald of Khmelnytskyi National University. Economic sciences 318, no. 3 (May 25, 2023): 180–85. http://dx.doi.org/10.31891/2307-5740-2023-318-3-28.

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Modern integrated economic statistics is a set of economic and statistical data that reflect a consistent and holistic picture of economic activity for political, commercial and other analytical purposes. In analyzing the historical aspect of the emergence and development of statistical practice, we have shown that its origin occurred around the time when the state emerged. There is information about the elementary recount (census) of population and land, which was conducted several millennia ago. With the formation of a centralized state and especially with its development, the scope of statistics has expanded significantly. It is established that the statistical support of effective development of enterprises of Ukraine in the scale of a particular industry can be realized according to official statistics, namely: statistical observations of the volume of sold industrial products and operating costs of sold products. It is emphasized that today the volume and structure of data presented on the official website of statistics of Ukraine, is considered insufficient for full information support of management processes of industrial enterprises. The approaches of scholars to the interpretation of the role of statistical methods of analysis for successful economic management are considered. The basic mathematical and statistical methods are listed and their short characteristic is given. It is established that the profitability indicator is chosen as a general indicator of efficiency and technological level of enterprises of the studied industries. Construction of the economic and mathematical model of profitability of products of separate industries made it possible to reveal production reserves to increase the efficiency of the enterprises. For mechanical engineering it means reducing the capital intensity of products used funds (depreciation). For metallurgy it means a reduction in material and labor intensity of products. For the production of chemical products it is reducing material consumption. When choosing statistical methods for studying socio-economic phenomena, it is necessary to take into account their interconnectedness and interdependence. Based on the results, the priority for the development of management decisions for the effective development of processing enterprises should be to increase the degree of processing of raw materials (to increase the technological level of production), increase productivity of fixed assets and increase final consumption.

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25

Zhao, Bin. "Statistical analysis on Alzheimer's disease." Journal of Infectious Diseases & Travel Medicine 7, no. 2 (October 31, 2023): 1–18. http://dx.doi.org/10.23880/jidtm-16000177.

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Alzheimer's disease is a progressive neurodegenerative disease that occurs mostly in the elderly and has memory impairment as the main clinical symptom. There is no ideal treatment for Alzheimer's disease, so early prevention is important. In this paper, we use brain structural information to diagnose Alzheimer's disease features and cognitive-behavioral characteristics, which is important for early and accurate diagnosis of mild cognitive impairment. To investigate the factors influencing Alzheimer's disease, a correlation analysis model was developed after preprocessing the missing values of the data. First, the data features were viewed, the missing values of the data were analyzed, and the useless features were removed and the missing values of the remaining features were filled with the average value. To verify the accuracy of the subsequent intelligent diagnosis model and clustering model, this paper divides the training set and test set according to PTID. Finally, the top ten important features are selected and the Spearman coefficients are chosen according to the distribution of the features for correlation analysis. Machine learning methods were utilized to build an Alzheimer's classification model to solve the problem of intelligent diagnosis of Alzheimer's disease. The pre-processed dataset in the above paper was trained with the model, and five methods of logistic regression, support vector machine, KNN classification, decision tree classification and XGB were utilized to build the classification model, and the accuracy, recall and F1 value of each model were visualized and compared, among which the accuracy of XGB model reached 83%, which is reasonable for the intelligent diagnosis of the disease. A K-Means-based clustering model for disease types was established using the K-Means clustering algorithm, clustering CN, MCI and AD into three major classes, and then refining MCI into three subclasses. The optimal K-values and random seeds were firstly found using the elbow principle, then the cluster analysis was performed using the feature values and data sets selected after preprocessing, and finally the MCI in MCI was extracted and sub-clustered into three subclasses SMC, EMCI and LMCI. In order to investigate the evolution pattern of different categories of diseases over time, patients with 3 categories of diseases are screened separately for analysis in this paper. Firstly, by combining the results above and reviewing the data, the features irrelevant to this task and columns containing a large number of missing values were removed, the remaining features were selected and probability density plots were drawn, and all discrete features and all features that were essentially zero were continued to be screened out. After that, the 15 features of CN, MCI and AD diseases were plotted separately over time to reveal their evolution patterns over time. We reviewed the relevant literature, sorted out and summarized the existing studies at home and abroad, and summarized the criteria for determining the five stages of Alzheimer's disease and the early intervention of the disease.

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26

Xue, Xuemei, and Jian Tao. "Statistical Order Convergence and Statistically Relatively Uniform Convergence in Riesz Spaces." Journal of Function Spaces 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/9092136.

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A new concept of statistically e-uniform Cauchy sequences is introduced to study statistical order convergence, statistically relatively uniform convergence, and norm statistical convergence in Riesz spaces. We prove that, for statistically e-uniform Cauchy sequences, these three kinds of convergence for sequences coincide. Moreover, we show that the statistical order convergence and the statistically relatively uniform convergence need not be equivalent. Finally, we prove that, for monotone sequences in Banach lattices, the norm statistical convergence coincides with the weak statistical convergence.

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27

Rosenholtz, R., B. J. Balas, A. Raj, L. Nakano, and L. Ilie. "The Visual System as Statistician: Statistical Representation in Early Vision." Journal of Vision 10, no. 7 (August 2, 2010): 23. http://dx.doi.org/10.1167/10.7.23.

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28

Bayzid, Md Shamsuzzoha, Siavash Mirarab, Bastien Boussau, and Tandy Warnow. "Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses." PLOS ONE 10, no. 6 (June 18, 2015): e0129183. http://dx.doi.org/10.1371/journal.pone.0129183.

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29

Ghosh, Sakti P. "Statistical relational tables for statistical database management." IEEE Transactions on Software Engineering SE-12, no. 12 (December 1986): 1106–16. http://dx.doi.org/10.1109/tse.1986.6313006.

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30

Peyghan, Esmaeil, Leila Nourmohammadifar, and Ion Mihai. "Statistical Submanifolds Equipped with F-Statistical Connections." Mathematics 12, no. 16 (August 12, 2024): 2492. http://dx.doi.org/10.3390/math12162492.

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This paper deals with statistical submanifolds and a family of statistical connections on them. The geometric structures such as the second fundamental form, curvatures tensor, mean curvature, statistical Ricci curvature and the relations among them on a statistical submanifold of a statistical manifold equipped with F-statistical connections are examined. The equations of Gauss and Codazzi of F-statistical connections are obtained. Such structures when the statistical submanifolds are conjugate symmetric are discussed. We present a inequality for statistical submanifolds in real space forms with respect to F-statistical connections. Also, we obtain a basic inequality involving statistical Ricci curvature and the squared F-mean curvature of a statistical submanifold of statistical manifolds.

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31

Ware, J. H. "Statistical practice and statistical education in cardiology." Circulation 75, no. 2 (February 1987): 307–10. http://dx.doi.org/10.1161/01.cir.75.2.307.

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32

Gigerenzer, Gerd. "We need statistical thinking, not statistical rituals." Behavioral and Brain Sciences 21, no. 2 (April 1998): 199–200. http://dx.doi.org/10.1017/s0140525x98281167.

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What Chow calls NHSTP is an inconsistent hybrid of Fisherian and Neyman-Pearsonian ideas. In psychology it has been practiced like ritualistic handwashing and sustained by wishful thinking about its utility. Chow argues that NHSTP is an important tool for ruling out chance as an explanation for data. I disagree. This ritual discourages theory development by providing researchers with no incentive to specify hypotheses.

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33

Poitevineau, Jacques, and Bruno Lecoutre. "Some statistical misconceptions in Chow's Statistical significance." Behavioral and Brain Sciences 21, no. 2 (April 1998): 215. http://dx.doi.org/10.1017/s0140525x98441165.

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Chow's book makes a provocative contribution to the debate on the role of statistical significance, but it involves some important misconceptions in the presentation of the Fisher and Neyman/Pearson's theories. Moreover, the author's caricature-like considerations about “Bayesianism” are completely irrelevant for discarding the Bayesian statistical theory. These facts call into question the objectivity of his contribution.

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34

Vosvrda, Miloslav S. "Statistical data analysis by dialogue statistical systems." Computational Statistics & Data Analysis 6, no. 2 (March 1988): 113–17. http://dx.doi.org/10.1016/0167-9473(88)90042-4.

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35

Hinchuk, L. I. "The Factors of Quality of Statistical Information Which is Formed by the State Statistics of Ukraine about Administrative Offenses." Business Inform 6, no. 497 (2019): 95–99. http://dx.doi.org/10.32983/2222-4459-2019-6-95-99.

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36

Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim, and Sophan Wahyudi Nawawi. "Statistical Analysis for Swarm Intelligence — Simplified." International Journal of Future Computer and Communication 4, no. 3 (2015): 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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37

Li, Ken W. "A Model of Teaching Statistical Computing." International Journal of Information and Education Technology 6, no. 2 (2016): 143–47. http://dx.doi.org/10.7763/ijiet.2016.v6.674.

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38

Anchan, Tanushri. "Crimes Against Women: A Statistical Analysis." Indian Journal of Applied Research 4, no. 2 (October 1, 2011): 8–9. http://dx.doi.org/10.15373/2249555x/feb2014/35.

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39

Doyle, E. Kevin, Vesa Tuomi, and Ian Rowley. "ICONE15-10441 Initiating Statistical Maintenance Optimization." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2007.15 (2007): _ICONE1510. http://dx.doi.org/10.1299/jsmeicone.2007.15._icone1510_234.

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40

Navatha, K., and V. V Hara Gopal. "Statistical Modelling for Health Insurance Data." International Journal of Science and Research (IJSR) 11, no. 11 (November 5, 2022): 1082–84. http://dx.doi.org/10.21275/sr221118184114.

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41

Srivastava, Uma. "Rough Statistical Convergence on Biquadratic Sequences." International Journal of Science and Research (IJSR) 11, no. 10 (October 5, 2022): 1081–85. http://dx.doi.org/10.21275/sr221022181930.

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42

HRYTSUN, Viktoriia. "STATISTICAL STUDY OF INTERNATIONAL FINANCIAL ASSISTANCE." Herald of Khmelnytskyi National University. Economic sciences 304, no. 2(2) (March 18, 2022): 307–12. http://dx.doi.org/10.31891/2307-5740-2022-304-2(2)-48.

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Investment is the primary catalyst for the development of any country. Without appropriate investment, recovery and a prosperous future for Ukraine are impossible. Despite thorough studies of domestic and foreign investment trends, more attention should be paid to analyzing the impact of crisis phenomena on the dynamics and structure of investments. The search for the main directions of activation of investment processes in conditions of the acute need for financial resources for the recovery of Ukraine requires further elaboration. The article aims to study the main trends, risks, and prospects for activating investment processes in Ukraine. The article analyzed the dynamics and studied the most important factors influencing the leading socio-economic indicators of Ukraine for 1997-2020. The level of investment intensity in the economy of Ukraine for 1995-2020 was evaluated, which showed that only within five years ( 2004-2008) the intensity of investments exceeded the bar of 20%. Ukraine never recovered after the global financial crisis of 2008-2009. Peculiarities of the influence of unpredictable factors and crisis phenomena on the volume and structure of investment attraction from various sources of financing have been studied. A study of the dynamics of foreign direct investments for 1995-2020 was conducted. The main directions and prerequisites for the activation of investments in the economy of the country, which is actually in a state of war, as well as its post-war recovery, were outlined. It was established that for the full-scale attraction of investments from investment funds, private investors, and grant funds, the availability of available and reliable information about promising investment projects and possible benefits from their implementation is of utmost importance; practical tools of public administration; a transparent system of project verification and an effective system of monitoring their results with the possibility of information exchange; minimization of corruption risks; working out the mechanism of insurance of investment risks during the war.

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43

Tani, Hiroaki, Kazumi Arahata, and Mamoru Umemura. "Statistical method." Journal of exercise physiology 5, no. 4 (1990): 221–31. http://dx.doi.org/10.1589/rika1986.5.221.

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44

Chung, Jaewon, Eric Bridgeford, Jesús Arroyo, Benjamin D. Pedigo, Ali Saad-Eldin, Vivek Gopalakrishnan, Liang Xiang, Carey E. Priebe, and Joshua T. Vogelstein. "Statistical Connectomics." Annual Review of Statistics and Its Application 8, no. 1 (March 7, 2021): 463–92. http://dx.doi.org/10.1146/annurev-statistics-042720-023234.

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The data science of networks is a rapidly developing field with myriad applications. In neuroscience, the brain is commonly modeled as a connectome, a network of nodes connected by edges. While there have been thousands of papers on connectomics, the statistics of networks remains limited and poorly understood. Here, we provide an overview from the perspective of statistical network science of the kinds of models, assumptions, problems, and applications that are theoretically and empirically justified for analysis of connectome data. We hope this review spurs further development and application of statistically grounded methods in connectomics.

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45

Rennolls, Keith, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 3 (1996): 622. http://dx.doi.org/10.2307/2983341.

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46

Crowder, Martin, P. H. Garthwaite, I. T. Jolliffe, and B. Jones. "Statistical Inference." Statistician 45, no. 3 (1996): 386. http://dx.doi.org/10.2307/2988478.

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47

Brunson, Barry W., and Vijay K. Rohatgi. "Statistical Inference." American Mathematical Monthly 94, no. 2 (February 1987): 210. http://dx.doi.org/10.2307/2322441.

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48

Lindley, D. V., and Vijay K. Rohatgi. "Statistical Inference." Mathematical Gazette 69, no. 447 (March 1985): 63. http://dx.doi.org/10.2307/3616474.

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49

Ashcraft, Alyce A. "Statistical Graffiti." Journal of Nursing Education 45, no. 1 (January 1, 2006): 44–45. http://dx.doi.org/10.3928/01484834-20060101-12.

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50

Caruso, Maria Girolama. "Statistical Appendix." WELFARE E ERGONOMIA, no. 1 (March 2017): 83–90. http://dx.doi.org/10.3280/we2016-001009.

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