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Articles de revues sur le sujet « Statistical analysis »

Pour voir les autres types de publications sur ce sujet consultez le lien suivant : Statistical analysis.

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 7 juillet 2024

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1

Begum, Hasina. « Statistical Analysis in GWAS ». International Journal of Science and Research (IJSR) 12, n^o 12 (5 décembre 2023) : 1072–78. http://dx.doi.org/10.21275/sr231215085226.

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2

Zhao, Bin. « Statistical analysis on Alzheimer's disease ». Journal of Infectious Diseases & ; Travel Medicine 7, n^o 2 (31 octobre 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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3

Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim et Sophan Wahyudi Nawawi. « Statistical Analysis for Swarm Intelligence — Simplified ». International Journal of Future Computer and Communication 4, n^o 3 (2015) : 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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4

Anchan, Tanushri. « Crimes Against Women : A Statistical Analysis ». Indian Journal of Applied Research 4, n^o 2 (1 octobre 2011) : 8–9. http://dx.doi.org/10.15373/2249555x/feb2014/35.

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5

Couturier, Raphaël, et Rubén Pazmiño. « Use of Statistical Implicative Analysis in Complement of Item Analysis ». International Journal of Information and Education Technology 6, n^o 1 (2016) : 39–43. http://dx.doi.org/10.7763/ijiet.2016.v6.655.

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6

Xue, Xuemei, et 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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7

Marcucci, Mark, et Sam Kachigan. « Statistical Analysis ». Technometrics 30, n^o 2 (mai 1988) : 235. http://dx.doi.org/10.2307/1270177.

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8

MUNRO, BARBARA HAZARD. « Statistical Analysis ». Clinical Nurse Specialist 3, n^o 3 (1989) : 113. http://dx.doi.org/10.1097/00002800-198900330-00004.

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9

Mnrcucci, Mark. « Statistical Analysis ». Technometrics 30, n^o 2 (mai 1988) : 235. http://dx.doi.org/10.1080/00401706.1988.10488379.

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10

Lewis, Roger J. « Statistical analysis ». Annals of Emergency Medicine 20, n^o 3 (mars 1991) : 323. http://dx.doi.org/10.1016/s0196-0644(05)80953-9.

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11

Kloke, John, et Johanna Hardin. « Statistical Analysis ». Current Protocols Essential Laboratory Techniques 00, n^o 1 (janvier 2008) : A.4A.1—A.4A.30. http://dx.doi.org/10.1002/9780470089941.eta04as00.

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12

Jumaev, Dilshod Qurbonmuratovich. « Statistical Analysis Of Investment In Construction Organizations ». American Journal of Management and Economics Innovations 3, n^o 06 (10 juin 2021) : 77–85. http://dx.doi.org/10.37547/tajmei/volume03issue06-12.

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In this article reveals the role of the construction sector in the economy. Also, performed economic and statistical analysis of the change in the housing stock and attracted investments in the construction sector. Scientific and practical proposals for solving problems formed in construction have been developed.

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13

Çakan, Celal, et Bilal Altay. « Statistically boundedness and statistical core of double sequences ». Journal of Mathematical Analysis and Applications 317, n^o 2 (mai 2006) : 690–97. http://dx.doi.org/10.1016/j.jmaa.2005.06.006.

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14

Kadilar, Gamze Ozel. « Statistical analysis of the foundation universities in Turkey ». New Trends and Issues Proceedings on Humanities and Social Sciences 4, n^o 3 (15 octobre 2017) : 101–8. http://dx.doi.org/10.18844/prosoc.v4i3.2521.

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15

Kassab, Ahmed S. « Statistical analysis of a Cretaceous Oyster from Egypt ». Neues Jahrbuch für Geologie und Paläontologie - Abhandlungen 182, n^o 2 (10 juin 1991) : 239–54. http://dx.doi.org/10.1127/njgpa/182/1991/239.

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16

Jumaev, Q. X. « STATISTICAL ANALYSIS OF THE EFFECTIVENESS OF AGRICULTURAL ACTIVITIES ». International Journal of Advance Scientific Research 03, n^o 06 (1 juin 2023) : 326–33. http://dx.doi.org/10.37547/ijasr-03-06-53.

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Agricultural activities play a crucial role in ensuring food security and sustainable development. Understanding the effectiveness of these activities is essential for optimizing resource allocation, increasing productivity, and addressing challenges such as climate change and population growth. This article presents a comprehensive statistical analysis of the effectiveness of agricultural activities, highlighting key methodologies, data sources, and analytical approaches. Through the examination of case studies and empirical evidence, this study aims to provide insights into the factors influencing agricultural effectiveness and inform decision-making processes in the agricultural sector.

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17

Le Fevre, Andrea. « Statistical Power Analysis ». Journal of the Royal Statistical Society : Series A (Statistics in Society) 168, n^o 2 (mars 2005) : 465. http://dx.doi.org/10.1111/j.1467-985x.2005.358_14.x.

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18

KIKUCHI, MACOTO, NOBUYASU ITO et YUTAKA OKABE. « STATISTICAL DEPENDENCE ANALYSIS ». International Journal of Modern Physics C 07, n^o 03 (juin 1996) : 379–87. http://dx.doi.org/10.1142/s0129183196000326.

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We review our recent studies on the dynamical correlations in MC simulations from the view point of the statistical dependence. Attentions are paid to the reduction of the statistical degrees of freedom for correlated data. Possible biases on several cumulants, such as the susceptibility and the Binder number due to finite MC length are discussed. A new method for calculating the equilibrium relaxation time from the analysis of the statistical dependence is presented. We apply it to the critical dynamics of the Ising model to estimate the dynamical critical exponent accurately.

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19

Jain, Sunil, et Vishwani Agrawal. « Statistical Fault Analysis ». IEEE Design & ; Test of Computers 2, n^o 1 (1985) : 38–44. http://dx.doi.org/10.1109/mdt.1985.294683.

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20

Cohen, Jacob. « Statistical Power Analysis ». Current Directions in Psychological Science 1, n^o 3 (juin 1992) : 98–101. http://dx.doi.org/10.1111/1467-8721.ep10768783.

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21

Yang, Yang, et Mi Chen. « Statistical analysis regarding ». PAIN 156, n^o 7 (juillet 2015) : 1366. http://dx.doi.org/10.1097/j.pain.0000000000000167.

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22

Belunis, Nancy. « Statistical Process Analysis ». Technometrics 43, n^o 2 (mai 2001) : 230–31. http://dx.doi.org/10.1198/004017001750386341.

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23

Ziegel, Eric. « Statistical Data Analysis ». Technometrics 31, n^o 2 (mai 1989) : 272. http://dx.doi.org/10.1080/00401706.1989.10488540.

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24

Mathew, Thomas. « Multivariate Statistical Analysis ». Technometrics 39, n^o 1 (février 1997) : 101. http://dx.doi.org/10.1080/00401706.1997.10485445.

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25

Conklin, Joseph. « Statistical Analysis Simplified ». Technometrics 42, n^o 2 (mai 2000) : 205. http://dx.doi.org/10.1080/00401706.2000.10486000.

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26

Owen, W. Jason. « Statistical Data Analysis ». Technometrics 42, n^o 3 (août 2000) : 311–12. http://dx.doi.org/10.1080/00401706.2000.10486057.

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27

Cales, Richard H. « Meaningful statistical analysis ». Annals of Emergency Medicine 15, n^o 10 (octobre 1986) : 1255. http://dx.doi.org/10.1016/s0196-0644(86)80905-2.

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28

Trenkler, Götz. « Multivariate statistical analysis ». Computational Statistics & ; Data Analysis 24, n^o 3 (mai 1997) : 372. http://dx.doi.org/10.1016/s0167-9473(97)87027-2.

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29

Bland, J. M. « Statistical analysis inappropriate ». BMJ 308, n^o 6924 (29 janvier 1994) : 339. http://dx.doi.org/10.1136/bmj.308.6924.339b.

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30

Copeland, Karen A. F. « Multivariate Statistical Analysis ». Journal of Quality Technology 29, n^o 4 (octobre 1997) : 494. http://dx.doi.org/10.1080/00224065.1997.11979809.

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31

Zimmer, Lora. « Statistical Process Analysis ». Journal of Quality Technology 33, n^o 3 (juillet 2001) : 384–85. http://dx.doi.org/10.1080/00224065.2001.11980090.

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32

Hassard, Tom, et Allan B. Becker. « Faulty statistical analysis ». Journal of Pediatrics 109, n^o 6 (décembre 1986) : 1075. http://dx.doi.org/10.1016/s0022-3476(86)80306-7.

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33

Williams, Ruth. « Statistical data analysis ». Nursing Management 23, n^o 1 (avril 2016) : 19. http://dx.doi.org/10.7748/nm.23.1.19.s22.

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34

Pagès, J., S. Berthelo, M. Brossier et D. Gourret. « Statistical penalty analysis ». Food Quality and Preference 32 (mars 2014) : 16–23. http://dx.doi.org/10.1016/j.foodqual.2013.07.008.

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35

Orlov, A. I. « Interval statistical analysis ». Journal of Mathematical Sciences 81, n^o 4 (septembre 1996) : 2851–57. http://dx.doi.org/10.1007/bf02362491.

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36

Dean, David. « Statistical Shape Analysis ». Journal of Human Evolution 38, n^o 3 (mars 2000) : 455–57. http://dx.doi.org/10.1006/jhev.1999.0391.

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37

Lachin, John M. « Nonparametric Statistical Analysis ». JAMA 323, n^o 20 (26 mai 2020) : 2080. http://dx.doi.org/10.1001/jama.2020.5874.

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38

Dubrovin, Valerii, Larysa Deineha et Anastasiya Yatsenko. « Statistical analysis software ». Electrical Engineering and Power Engineering, n^o 3 (23 novembre 2023) : 25–32. http://dx.doi.org/10.15588/1607-6761-2023-3-3.

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Purpose. Analysis of existing software to perform statistical analysis for further use as part of the selection of the necessary software for data processing. Methodology. To conduct a software review, an analysis of scientific articles and open sources on statistical analysis software was conducted. Findings. Choosing the right statistical software is a key decision in the field of data analysis, with numerous options to meet a variety of needs. This article provides a comprehensive overview of five leading statistical software tools: IBM SPSS Statistics, RStudio, Stata, Minitab, and Python. This paper reveals key insights into the capabilities, functions, and suitability of each tool for various analytical tasks. This review concludes that the choice of statistical software should be consistent with specific project requirements, data complexity, and user experience. Researchers and analysts should consider their analytical goals and preferences when choosing the most appropriate tool. In addition, to make informed decisions in this dynamic field, it is important to stay abreast of new trends in data analysis and machine learning. Originality. The conducted analysis revealed the possibilities and application of the most popular software for solving problems of statistical analysis. The work provides a comprehensive overview of current trends and innovations in the field of software for statistical analysis, offering readers a deeper understanding of existing tools. Practical value. The conducted analysis will allow to choose software for solving a specific task of statistical analysis based on its characteristics and existing requirements. This work helps to identify the practical benefits of statistical analysis software and promotes the implementation of these tools in various fields of activity, providing improvements in analysis and decision-making processes.

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39

Vosvrda, Miloslav S. « Statistical data analysis by dialogue statistical systems ». Computational Statistics & ; Data Analysis 6, n^o 2 (mars 1988) : 113–17. http://dx.doi.org/10.1016/0167-9473(88)90042-4.

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40

Torres-Carrasco, M., J. G. Palomo et F. Puertas. « Sodium silicate solutions from dissolution of glasswastes. Statistical analysis ». Materiales de Construcción 64, n^o 314 (30 mars 2014) : e014. http://dx.doi.org/10.3989/mc.2014.05213.

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41

MILOSAN, Ioan. « STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE ». SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, n^o 1 (24 juin 2016) : 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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42

Gai, Oleksandr, et Olena Magopets. « Statistical Problems and Imperatives in Wartime : a Critical Analysis ». Central Ukrainian Scientific Bulletin. Economic Sciences, n^o 10(43) (2023) : 44–51. http://dx.doi.org/10.32515/2663-1636.2023.10(43).44-51.

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The article highlights the modern perspectives of statistics as a science and its role in various fields of human activity. The article is based on the analysis of current trends in the development of statistics, as well as its significance in the era of the digital revolution and the information age. The prospects of statistics as a science are incredibly promising and diverse, covering different fields and industries. Through a broad analysis of current trends and achievements, this article sheds light on the development of statistics and its potential impact on shaping the future. As a result of the study, the main prospects for the development of statistics as a science and a field of practical activity were determined, and the key aspects emphasizing its importance were formulated. It has been proven that statistics is a dynamic, evolving discipline with enormous potential for innovation, discovery, and impact on society. The critical analysis of statistical problems and urgent tasks that arise in wartime has been provided. The role of statistics in those military actions is considered, and the application of statistical methods to strategic and tactical decision-making is analyzed. In times of war, statistics face numerous challenges that can complicate the collection, analysis, and interpretation of data. During conflict, the role of statistics becomes paramount in informing decision-making, resource allocation, and assessing the impact of military action. However, applying statistical methods in wartime poses unique challenges, from collecting data in a hostile environment to ensuring the accuracy and reliability of analysis in the face of uncertainty and misinformation. This article examines the multifaceted challenges that arise when using statistics in wartime scenarios, highlighting the implications for military strategy, humanitarian efforts, and postwar reconstruction. By addressing these challenges, statisticians can make a significant contribution to improving the effectiveness and ethical conduct of operations in conflict zones. The prospects for further research in the study of indirect methods and the use of alternative sources of data collection, for the full realization of the functions of statistics in wartime conditions is substantiated.

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43

Dhakre, D. S., et D. Bhattacharya. « Statistical Analysis Step-By-Step Using Statistical Calculator ». International Journal of Current Microbiology and Applied Sciences 7, n^o 11 (10 novembre 2018) : 901–21. http://dx.doi.org/10.20546/ijcmas.2018.711.107.

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44

Bera, S., et B. Ch Tripathy. « STATISTICAL BOUNDED SEQUENCES OF BI-COMPLEX NUMBERS ». Issues of Analysis 30, n^o 2 (juin 2023) : 3–16. http://dx.doi.org/10.15393/j3.art.2023.13090.

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45

Aizawa, Naoyuki, et Emiko Nakasone. « Statistical Method. Correlation Analysis ». Journal of exercise physiology 4, n^o 4 (1989) : 223–29. http://dx.doi.org/10.1589/rika1986.4.223.

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46

Haigh, John, R. A. Johnson et D. W. Wichern. « Applied Multivariate Statistical Analysis ». Mathematical Gazette 72, n^o 462 (décembre 1988) : 331. http://dx.doi.org/10.2307/3619964.

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47

Gupal, Anatoliy M., Ivan I. Andreychuk, Alexandra A. Vagis et Ludmila A. Zakrevskaya. « Statistical Analysis of Proteins ». Journal of Automation and Information Sciences 36, n^o 12 (2004) : 25–29. http://dx.doi.org/10.1615/jautomatinfscien.v36.i12.20.

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48

Ziegel, Eric R., Richard A. Johnson et Dean W. Wichern. « Applied Multivariate Statistical Analysis ». Technometrics 41, n^o 1 (février 1999) : 81. http://dx.doi.org/10.2307/1271011.

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49

Lachenbruch, P. A., L. Lebart, A. Morineau et K. M. Warwick. « Multivariate Descriptive Statistical Analysis. » Biometrics 41, n^o 2 (juin 1985) : 592. http://dx.doi.org/10.2307/2530887.

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50

Kumar, Anil. « Statistical analysis and ophthalmology ». Indian Journal of Clinical and Experimental Ophthalmology 2, n^o 2 (2016) : 82. http://dx.doi.org/10.5958/2395-1451.2016.00020.2.

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