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Journal articles on the topic 'Statistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2024

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1

Durbin, J. "Statistics and Statistical Science." Journal of the Royal Statistical Society. Series A (General) 150, no. 3 (1987): 177. http://dx.doi.org/10.2307/2981472.

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2

Fernholz, Robert, and Cary Maguire. "The Statistics of Statistical Arbitrage." Financial Analysts Journal 63, no. 5 (September 2007): 46–52. http://dx.doi.org/10.2469/faj.v63.n5.4839.

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3

Fernholz, Robert, and Cary Maguire. "The Statistics of Statistical Arbitrage." CFA Digest 38, no. 1 (February 2008): 83–84. http://dx.doi.org/10.2469/dig.v38.n1.34.

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4

Pant, N., A. Rotti, and T. Souradeep. "Statistics of statistical anisotropy measures." Journal of Physics: Conference Series 484 (March 5, 2014): 012046. http://dx.doi.org/10.1088/1742-6596/484/1/012046.

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5

Nelder, John A. "From Statistics to Statistical Science." Journal of the Royal Statistical Society: Series D (The Statistician) 48, no. 2 (July 1999): 257–69. http://dx.doi.org/10.1111/1467-9884.00187.

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6

BEYTH-MAROM, RUTH, FIONA FIDLER, and GEOFF CUMMING. "STATISTICAL COGNITION: TOWARDS EVIDENCE-BASED PRACTICE IN STATISTICS AND STATISTICS EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 7, no. 2 (November 29, 2008): 20–39. http://dx.doi.org/10.52041/serj.v7i2.468.

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Practitioners and teachers should be able to justify their chosen techniques by taking into account research results: This is evidence-based practice (EBP). We argue that, specifically, statistical practice and statistics education should be guided by evidence, and we propose statistical cognition (SC) as an integration of theory, research, and application to support EBP. SC is an interdisciplinary research field, and a way of thinking. We identify three facets of SC—normative, descriptive, and prescriptive— and discuss their mutual influences. Unfortunately, the three components are studied by somewhat separate groups of scholars, who publish in different journals. These separations impede the implementation of EBP. SC, however, integrates the facets and provides a basis for EBP in statistical practice and education. First published November 2008 at Statistics Education Research Journal: Archives
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7

BEYTH-MAROM, RUTH, FIONA FIDLER, and GEOFF CUMMING. "STATISTICAL COGNITION: TOWARDS EVIDENCE-BASED PRACTICE IN STATISTICS AND STATISTICS EDUCATION." STATISTICS EDUCATION RESEARCH JOURNAL 7, no. 2 (November 29, 2008): 20–39. http://dx.doi.org/10.52041/serj.v7i2.468.

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Practitioners and teachers should be able to justify their chosen techniques by taking into account research results: This is evidence-based practice (EBP). We argue that, specifically, statistical practice and statistics education should be guided by evidence, and we propose statistical cognition (SC) as an integration of theory, research, and application to support EBP. SC is an interdisciplinary research field, and a way of thinking. We identify three facets of SC—normative, descriptive, and prescriptive— and discuss their mutual influences. Unfortunately, the three components are studied by somewhat separate groups of scholars, who publish in different journals. These separations impede the implementation of EBP. SC, however, integrates the facets and provides a basis for EBP in statistical practice and education. First published November 2008 at Statistics Education Research Journal: Archives
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8

SAIDI, SITI SHAHIRAH, and NYET MOI SIEW. "ASSESSING SECONDARY SCHOOL STUDENTS’ STATISTICAL REASONING, ATTITUDE TOWARDS STATISTICS, AND STATISTICS ANXIETY." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 1 (February 28, 2022): 6. http://dx.doi.org/10.52041/serj.v21i1.67.

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Assessment on statistical reasoning is an area of academic interest in statistics education research in tandem with attitudes and anxiety towards statistics, since many studies report students are likely to encounter problems with statistics due to these two non-cognitive factors. In this study, 320 Tenth Grade science stream students from Sabah, Malaysia were tested using the Statistical Reasoning Test Survey (SRTS), the Survey of Attitudes towards Statistics (SATS), and the Statistical Anxiety Scale (SAS), which assessed their statistical reasoning, attitude, and anxiety, respectively. Generally, the findings revealed the students held i) a quantitative level in statistical reasoning, ii) a positive attitude towards statistics, and iii) a moderate level of statistics anxiety. A positive relationship between attitudes towards statistics and statistical reasoning, and a negative relationship between statistics anxiety and statistical reasoning were also observed. The Value, Interest, and Interpretation Anxiety components were predictor variables for statistical reasoning.
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9

Klaus, Bernd. "Statistical relevance—relevant statistics, part I." EMBO Journal 34, no. 22 (September 21, 2015): 2727–30. http://dx.doi.org/10.15252/embj.201592958.

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10

Il'ya, Matveev, Savchenko Dmitrii, and Ulyanova Vera. "Legal statistics and the statistical jurisprudence." Ideas and Ideals 2, no. 4 (December 21, 2015): 51–58. http://dx.doi.org/10.17212/2075-0862-2015-4.2-51-58.

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11

Wada, Yasuhiko, and Nobuhisa Kashiwagi. "Selecting Statistical Models with Information Statistics." Journal of Dairy Science 73, no. 12 (December 1990): 3575–82. http://dx.doi.org/10.3168/jds.s0022-0302(90)79058-3.

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12

Oldham, Jackie. "Statistical tests (Part 1): descriptive statistics." Nursing Standard 7, no. 43 (July 14, 1993): 30–35. http://dx.doi.org/10.7748/ns.7.43.30.s49.

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13

Booth, J., and W. Mudryk. "Statistical quality control at statistics canada." Communications in Statistics - Theory and Methods 14, no. 11 (January 1985): 2589–603. http://dx.doi.org/10.1080/03610928508829063.

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14

Leech, Nancy L. "Statistics Poker: Reinforcing Basic Statistical Concepts." Teaching Statistics 30, no. 1 (April 16, 2008): 26–28. http://dx.doi.org/10.1111/j.1467-9639.2007.00309.x.

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15

Leech, Nancy L. "Statistics Poker: Reinforcing Basic Statistical Concepts." Teaching Statistics 30, no. 1 (January 2008): 26–28. http://dx.doi.org/10.1111/j.1467-9639.2008.00309.x.

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16

Hoover, Donald R. "Basic Statistics and Pharmaceutical Statistical Applications." Journal of Biopharmaceutical Statistics 25, no. 1 (January 2, 2015): 226–29. http://dx.doi.org/10.1080/10543406.2015.985162.

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17

Ludbrook, John, and Hugh Dudley. "ISSUES IN BIOMEDICAL STATISTICS: STATISTICAL INFERENCE." ANZ Journal of Surgery 64, no. 9 (September 1994): 630–36. http://dx.doi.org/10.1111/j.1445-2197.1994.tb02308.x.

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18

Clyde, Merlise. "The Statistical Exorcist: Dispelling Statistics Anxiety." Technometrics 36, no. 3 (August 1994): 330. http://dx.doi.org/10.1080/00401706.1994.10485827.

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19

Parvan, A. S., and T. S. Biró. "Rényi statistics in equilibrium statistical mechanics." Physics Letters A 374, no. 19-20 (April 2010): 1951–57. http://dx.doi.org/10.1016/j.physleta.2010.03.007.

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20

Li, Ning. "Basic Statistics and Pharmaceutical Statistical Applications." Controlled Clinical Trials 21, no. 6 (December 2000): 593–94. http://dx.doi.org/10.1016/s0197-2456(00)00090-8.

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21

Obremski, Thomas E. "The Statistical Exorcist: Dispelling Statistics Anxiety." Journal of Quality Technology 17, no. 3 (July 1985): 172. http://dx.doi.org/10.1080/00224065.1985.11978961.

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22

McCluskey, Anthony, and Abdul Ghaaliq Lalkhen. "Statistics III: Probability and statistical tests." Continuing Education in Anaesthesia Critical Care & Pain 7, no. 5 (October 2007): 167–70. http://dx.doi.org/10.1093/bjaceaccp/mkm028.

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23

Barlow, Richard E., Myles Hollander, and Frank Proschan. "The Statistical Exorcist: Dispelling Statistics Anxiety." Journal of the American Statistical Association 81, no. 393 (March 1986): 258. http://dx.doi.org/10.2307/2288009.

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24

Orlov, A. I. "STATISTICAL SIMULATIONS METHOD IN APPLIED STATISTICS." Industrial laboratory. Diagnostics of materials 85, no. 5 (June 5, 2019): 67–79. http://dx.doi.org/10.26896/1028-6861-2019-85-5-67-79.

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The new paradigm of mathematical research methods is based on the effective application of information and communication technologies both in calculating the characteristics of the methods of data analysis and in simulation modeling. Pseudo-random number generators underlie many modern data analysis technologies. To solve specific applied problems, researchers permanently develop the new methods for processing statistical data, i.e., measurement results (observations, tests, analyzes, experiments) and expert estimations. The properties of each newly proposed method must be studied. The intellectual tools are limit theorems and method of statistical simulations (Monte-Carlo method). In 2016, our journal opened a discussion on the current state and prospects for the development of statistical modeling, i.e., the theory and practice of applicating the method of the statistical simulations (Monte-Carlo method), and various variants of the simulation. The previous discussion about the properties of such generators was conducted in our journal in 1985 - 1993. This article is devoted to application of the statistical simulations method to the study of the properties of statistical criteria for testing the homogeneity of two independent samples. We consider: the Kramer - Welch criterion, which coincides with Student's criterion when sample sizes are equal; the criteria of Lord, Wilcoxon (Mann - Whitney), Wolfowitz, Van der Waerden, Smirnov, со 2 (Lehmann - Rosenblatt). It is necessary to set the distribution functions of the elements of two samples. We use the normal and Weibull - Gnedenko distributions. It is shown advisable to use the Lehmann - Rosenblatt со 2 test when testing the hypothesis of coincidence of the distribution functions of two samples. If there is a reason to assume that the distributions differ mainly in the shift, then the Wilcoxon and Van der Waerden criteria can be used. However, even in this case, the со 2 test may be more powerful. In the general case, apart from the Lehmann - Rosenblatt criterion, the use of the Smirnov criterion is permissible, taking into account the difference between the real level of significance and the nominal one. The frequency of the discrepancies of statistical findings based on different criteria is studied.
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25

Anagboso, Mavis, Allan Flowers, Geoff Tily, and Gavin Wallis. "Official statistical publications and economic statistics." Economic & Labour Market Review 1, no. 1 (January 2007): 19–26. http://dx.doi.org/10.1057/palgrave.elmr.1410007.

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26

Martynov, G. V. "Probabilistic-statistical programs from ?applied statistics?" Journal of Soviet Mathematics 50, no. 3 (June 1990): 1643–84. http://dx.doi.org/10.1007/bf01096290.

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27

Kaufman, Jay S. "Statistics, Adjusted Statistics, and Maladjusted Statistics." American Journal of Law & Medicine 43, no. 2-3 (May 2017): 193–208. http://dx.doi.org/10.1177/0098858817723659.

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Statistical adjustment is a ubiquitous practice in all quantitative fields that is meant to correct for improprieties or limitations in observed data, to remove the influence of nuisance variables or to turn observed correlations into causal inferences. These adjustments proceed by reporting not what was observed in the real world, but instead modeling what would have been observed in an imaginary world in which specific nuisances and improprieties are absent. These techniques are powerful and useful inferential tools, but their application can be hazardous or deleterious if consumers of the adjusted results mistake the imaginary world of models for the real world of data. Adjustments require decisions about which factors are of primary interest and which are imagined away, and yet many adjusted results are presented without any explanation or justification for these decisions. Adjustments can be harmful if poorly motivated, and are frequently misinterpreted in the media’s reporting of scientific studies. Adjustment procedures have become so routinized that many scientists and readers lose the habit of relating the reported findings back to the real world in which we live.
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28

van den Besselaar, Peter. "Descriptive statistics, inferential statistics, rhetorical statistics." Journal of the American Society for Information Science and Technology 54, no. 11 (2003): 1077. http://dx.doi.org/10.1002/asi.10304.

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29

Subbarao, Dr Uppu Venkata. "Order Statistics of Additive Uniform Exponential Distribution." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1084–87. http://dx.doi.org/10.22214/ijraset.2021.38573.

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Abstract: In this paper we investigated the order statistics by using Additive Uniform Exponential Distribution (AUED) proposed by Venkata Subbarao Uppu (2010).The probability density functions of rth order Statistics, lth moment of the rth order Statistic, minimum, maximum order statistics, mean of the maximum and minimum order statistics, the joint density function of two order statistics were calculated and discussed in detailed . Applications and several aspects were discussed Keywords: Additive Uniform Exponential Distribution, Moments, Minimum order statistic, Maximum order statistic, Joint density of the order Statistics, complete length of service.
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30

Moghimi, Maryam, and Herbert W. Corley. "Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions." Data 5, no. 3 (September 13, 2020): 84. http://dx.doi.org/10.3390/data5030084.

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In this paper, we study the information lost when a real-valued statistic is used to reduce or summarize sample data from a discrete random variable with a one-dimensional parameter. We compare the probability that a random sample gives a particular data set to the probability of the statistic’s value for this data set. We focus on sufficient statistics for the parameter of interest and develop a general formula independent of the parameter for the Shannon information lost when a data sample is reduced to such a summary statistic. We also develop a measure of entropy for this lost information that depends only on the real-valued statistic but neither the parameter nor the data. Our approach would also work for non-sufficient statistics, but the lost information and associated entropy would involve the parameter. The method is applied to three well-known discrete distributions to illustrate its implementation.
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31

Linhart, Aleš. "(Statistically speaking, sports is healthy for your heart, but statistics can be tricky)." Cor et Vasa 62, no. 4 (September 16, 2020): 351–52. http://dx.doi.org/10.33678/cor.2020.079.

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32

KADOYA, Chitoshi. "Road Accident Statistics." Journal of UOEH 10, no. 2 (1988): 233. http://dx.doi.org/10.7888/juoeh.10.233.

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33

Rasch, D., and M. L. Tiku. "Robustness of Statistical Methods and Nonparametric Statistics." Biometrics 42, no. 3 (September 1986): 684. http://dx.doi.org/10.2307/2531231.

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34

Loosmore, N. Bert, and E. David Ford. "STATISTICAL INFERENCE USING THEGORKPOINT PATTERN SPATIAL STATISTICS." Ecology 87, no. 8 (August 2006): 1925–31. http://dx.doi.org/10.1890/0012-9658(2006)87[1925:siutgo]2.0.co;2.

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35

Rohana, Rohana, and Yunika Lestaria Ningsih. "STUDENTS’ STATISTICAL REASONING IN STATISTICS METHOD COURSE." Jurnal Pendidikan Matematika 14, no. 1 (December 31, 2019): 81–90. http://dx.doi.org/10.22342/jpm.14.1.6732.81-90.

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through descriptive quantitative. The results of data analysis showed that 32% of students had level 1 statistical reasoning (the lowest level), 20% were at level 2, 28% at level 3, 12% at level 4 and 8% at level 5 (highest level). Based on the result, it can conclude that students' statistical reasoning ability in learning statistical method is not satisfactory, students are still very lacking in reasoning.
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36

Holt, Lori L., and Andrew J. Lotto. "What are the statistics in statistical learning?" Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2444. http://dx.doi.org/10.1121/1.4779327.

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37

Gentle, James E. "Courses in Statistical Computing and Computational Statistics." American Statistician 58, no. 1 (February 2004): 2–5. http://dx.doi.org/10.1198/0003130042908.

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38

Parvan, A. S., and T. S. Biró. "Equilibrium statistical mechanics for incomplete nonextensive statistics." Physics Letters A 375, no. 3 (January 2011): 372–78. http://dx.doi.org/10.1016/j.physleta.2010.12.022.

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39

Wang, Qiuping A. "Incomplete statistics: nonextensive generalizations of statistical mechanics." Chaos, Solitons & Fractals 12, no. 8 (June 2001): 1431–37. http://dx.doi.org/10.1016/s0960-0779(00)00113-2.

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40

Ziegel, Eric. "Robustness of Statistical Methods and Nonparametric Statistics." Technometrics 31, no. 2 (May 1989): 267–68. http://dx.doi.org/10.1080/00401706.1989.10488533.

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41

Régnier, Jean-Claude, and Ekaterina Kuznetsova. "Teaching of Statistics: Formation of Statistical Reasoning." Procedia - Social and Behavioral Sciences 154 (October 2014): 99–103. http://dx.doi.org/10.1016/j.sbspro.2014.10.119.

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42

Markiv, B. B., R. M. Tokarchuk, P. P. Kostrobij, and M. V. Tokarchuk. "Nonequilibrium statistical operator method in Renyi statistics." Physica A: Statistical Mechanics and its Applications 390, no. 5 (March 2011): 785–91. http://dx.doi.org/10.1016/j.physa.2010.11.009.

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43

Rao, T. J. "National statistical commission and Indian official statistics." Resonance 18, no. 12 (December 2013): 1062–72. http://dx.doi.org/10.1007/s12045-013-0134-z.

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44

Isakov, S. B. "Generalization of quantum statistics in statistical mechanics." International Journal of Theoretical Physics 32, no. 5 (May 1993): 737–67. http://dx.doi.org/10.1007/bf00671663.

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45

Darby, Keith V., and K. Hinkelmann. "Experimental Design, Statistical Models, and Genetic Statistics." Applied Statistics 34, no. 1 (1985): 79. http://dx.doi.org/10.2307/2347889.

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46

Kimber, Alan, D. Rasch, and M. L. Tiku. "Robustness of Statistical Methods and Nonparametric Statistics." Journal of the Royal Statistical Society. Series A (General) 150, no. 3 (1987): 282. http://dx.doi.org/10.2307/2981481.

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47

Curran-Everett, Douglas. "Explorations in statistics: statistical facets of reproducibility." Advances in Physiology Education 40, no. 2 (June 2016): 248–52. http://dx.doi.org/10.1152/advan.00042.2016.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eleventh installment of Explorations in Statistics explores statistical facets of reproducibility. If we obtain an experimental result that is scientifically meaningful and statistically unusual, we would like to know that our result reflects a general biological phenomenon that another researcher could reproduce if (s)he repeated our experiment. But more often than not, we may learn this researcher cannot replicate our result. The National Institutes of Health and the Federation of American Societies for Experimental Biology have created training modules and outlined strategies to help improve the reproducibility of research. These particular approaches are necessary, but they are not sufficient. The principles of hypothesis testing and estimation are inherent to the notion of reproducibility in science. If we want to improve the reproducibility of our research, then we need to rethink how we apply fundamental concepts of statistics to our science.
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48

Malaguerra, Carlo, and Raul Suarez de Miguel. "Cantonal statistics and federal statistics: process of integration of the statistical system of Switzerland." Statistical Journal of the United Nations Economic Commission for Europe 14, no. 1 (February 1, 1997): 89–104. http://dx.doi.org/10.3233/sju-1997-14109.

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49

Schulte Nordholt, Eric. "Statistical disclosure control (SDC) in practice: some examples in official statistics of Statistics Netherlands." Statistical Journal of the United Nations Economic Commission for Europe 18, no. 4 (December 28, 2001): 321–28. http://dx.doi.org/10.3233/sju-2001-18406.

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50

Zipfel, Adreas, and Paul Grob. "How Much Detail on Confirmatory Statistics and Exploratory Statistics Must a Statistical Report Contain?" Drug Information Journal 29, no. 2 (April 1995): 479–81. http://dx.doi.org/10.1177/009286159502900218.

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