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Journal articles on the topic 'Chemistry – Statistical methods'

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Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

Grieve, A. P., P. C. Meier, and R. E. Zund. "Statistical Methods in Analytical Chemistry." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 2 (1994): 311. http://dx.doi.org/10.2307/2983374.

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2

Meier, P. C., and R. E. Zund. "Statistical Methods in Analytical Chemistry." Biometrics 50, no. 3 (September 1994): 896. http://dx.doi.org/10.2307/2532821.

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3

Morton, Michael J. "Statistical Methods in Applied Chemistry." Technometrics 35, no. 2 (May 1993): 228–29. http://dx.doi.org/10.1080/00401706.1993.10485055.

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4

Hillyer, Martin. "Statistical Methods in Analytical Chemistry." Technometrics 37, no. 1 (February 1995): 113–14. http://dx.doi.org/10.1080/00401706.1995.10485895.

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5

Czermiński, J., A. Iwasiewicz, Z. Paszek, A. Sikorski, and Richard G. Brereton. "Statistical methods in applied chemistry." Analytica Chimica Acta 244 (1991): 296. http://dx.doi.org/10.1016/s0003-2670(00)82518-0.

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6

Ziegel, Eric R., and J. Einax. "Chemometrics in Environmental Chemistry: Statistical Methods." Technometrics 38, no. 4 (November 1996): 412. http://dx.doi.org/10.2307/1271332.

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7

Whitbeck, Michael. "Chemometrics in environmental chemistry, statistical methods." Chemometrics and Intelligent Laboratory Systems 34, no. 1 (August 1996): 131–32. http://dx.doi.org/10.1016/0169-7439(96)00008-1.

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8

Muranaka, Ken. "Teaching Statistical Methods." Journal of Chemical Education 76, no. 4 (April 1999): 469. http://dx.doi.org/10.1021/ed076p469.1.

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9

Hart, Brian, Mark Biesinger, and Roger St C. Smart. "Improved statistical methods applied to surface chemistry in minerals flotation." Minerals Engineering 19, no. 6-8 (May 2006): 790–98. http://dx.doi.org/10.1016/j.mineng.2005.09.039.

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10

Smit, H. C. "Statistical methods in analytical chemistry (Chemical Analysis Series, Vol. 123)." Journal of Chromatography A 670, no. 1-2 (June 1994): 245–46. http://dx.doi.org/10.1016/0021-9673(94)80303-x.

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11

WESTMAN, A. E. R. "STATISTICAL METHODS IN CERAMIC RESEARCH1." Journal of the American Ceramic Society 10, no. 3 (June 28, 2008): 133–47. http://dx.doi.org/10.1111/j.1151-2916.1927.tb19756.x.

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12

Miller, James N. "Basic statistical methods for Analytical Chemistry. Part 2. Calibration and regression methods. A review." Analyst 116, no. 1 (1991): 3. http://dx.doi.org/10.1039/an9911600003.

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13

Muranaka, Ken. "Teaching Statistical Methods (the author replies)." Journal of Chemical Education 76, no. 4 (April 1999): 469. http://dx.doi.org/10.1021/ed076p469.2.

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14

Meloun, Milan. "Statistical methods in analytical chemistry, by P.C. Meier and R.E. Zund." Chemometrics and Intelligent Laboratory Systems 22, no. 2 (February 1994): 284–85. http://dx.doi.org/10.1016/0169-7439(94)80008-1.

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15

Mujumdar, Dr A. S. "Statistical Methods for Physical Science." Drying Technology 13, no. 8-9 (January 1995): 2253–54. http://dx.doi.org/10.1080/07373939508917086.

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16

Wright, Robert A. "Three Evidence Based Methods to Compensate for a Lack of Subject Background when Ordering Chemistry Monographs." Evidence Based Library and Information Practice 3, no. 3 (September 10, 2008): 3. http://dx.doi.org/10.18438/b8v329.

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Objective – The aim of this article is to present evidence based methods for the selection of chemistry monographs, particularly for librarians lacking a background in chemistry. These methods will be described in detail, their practical application illustrated, and their efficacy tested by analyzing circulation data. Methods – Two hundred and ninety-five chemistry monographs were selected between 2005 and 2007 using rigorously-applied evidence based methods involving the Library's integrated library system (ILS), Google, and SciFinder Scholar. The average circulation rate of this group of monographs was compared to the average circulation rate of 254 chemistry monographs selected between 2002 and 2004 when the methods were not used or were in an incomplete state of development. Results – Circulations/month were on average 9% greater in the cohort of monographs selected with the rigorously-applied evidence based methods. Further statistical analysis, however, finds that this result can not be attributed to the different application of these methods. Conclusion – The methods discussed in this article appear to provide an evidence base for the selection of chemistry monographs, but their application does not change circulation rates in a statistically significant way. Further research is needed to determine if this lack of statistical significance is real or a product of the organic development and application of these methods over time, making definitive comparisons difficult.

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17

ANEZAKI, Katsunori, Katsuyuki YAMAGUCHI, Shosuke NATSUME, Riki IWATA, and Shunji HASHIMOTO. "Estimation of PCB Sources Using Statistical Methods." BUNSEKI KAGAKU 56, no. 8 (2007): 639–48. http://dx.doi.org/10.2116/bunsekikagaku.56.639.

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18

Boyd, James C., Nader Rifai, and Thomas Annesley. "Statistical Methods for Test and Biomarker Evaluation Studies: A Clinical Chemistry Series." Clinical Chemistry 58, no. 9 (September 1, 2012): 1273–74. http://dx.doi.org/10.1373/clinchem.2012.192252.

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19

Simeonov, Vasil. "Basic Multivariate Statistical Methods for Environmental Monitoring Data Mining: Introductory Course for Master Students." Chemistry-Didactics-Ecology-Metrology 25, no. 1-2 (December 1, 2020): 35–56. http://dx.doi.org/10.2478/cdem-2020-0002.

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Abstract The present introductory course of lectures summarizes the principles and algorithms of several widely used multivariate statistical methods: cluster analysis, principal components analysis, principal components regression, N-way principal components analysis, partial least squares regression and self-organizing maps with respect to their possible application in intelligent analysis, classification, modelling and interpretation to environmental monitoring data. The target group of possible users is master program students (environmental chemistry, analytical chemistry, environmental modelling and risk assessment etc.).

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20

Bielińska-Wąż, Dorota, Piotr Wąż, and Subhash C. Basak. "Similarity studies using statistical and genetical methods." Journal of Mathematical Chemistry 42, no. 4 (October 10, 2006): 1003–13. http://dx.doi.org/10.1007/s10910-006-9155-0.

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21

Derringer, George C. "Statistical Methods in Rubber Research and Development." Rubber Chemistry and Technology 61, no. 3 (July 1, 1988): 377–421. http://dx.doi.org/10.5254/1.3536194.

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Abstract As this article has demonstrated, statistical methods have considerable utility in rubber research and development. Such methods are cost effective and perhaps, more important, help the user to formulate questions in such a way as to make research programs more productive. The use of statistical methods in formulation development have been particularly fruitful. For example, without statistical methods, the compounders common task of simultaneously achieving a specific balance of cost, property levels, and processing behavior would be an expensive undertaking at best and hopeless at worst. Statistical methods help the practitioner make sound decisions in the light of often extreme variability in the data. This is especially the case when the data is fatigue, tensile strength, or some other fracture property of rubber. In the light of such broad distributions, hit or miss and one variable at a time strategies are seriously flawed. It is not hard to find a sequential study in which decisions about each trial were driven simply by changes due to random variability which were erroneously attributed to the intentional changes in variable level(s) from the previous trial. Such research studies are very similar to random walks. Many excellent and innovative studies are reported above. However, given the wide utility of statistical methods, these methods appear to be underutilized judging from the unexpectedly small number of papers which were found. Similarly, it is not difficult to find published rubber research which would not have benefitted by the use of statistical experimental design and/or data analysis. Fortunately, however, this appears to be changing. The increasing availability of good affordable software for personal computers makes it possible for good statistics to be done with less dependence on trained statisticians which are not available in many organizations. As this trend continues, the number of studies employing statistical methods will surely grow.

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22

Deming, S. N. "Chemometrics: an overview." Clinical Chemistry 32, no. 9 (September 1, 1986): 1702–6. http://dx.doi.org/10.1093/clinchem/32.9.1702.

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Abstract Chemometrics is broadly defined as the application of mathematical and statistical methods to chemistry. Because the mathematical and statistical aspects of chemistry require measured values, analytical chemists have been at the forefront of the "chemometric revolution." Using the analysis of variance as a paradigm, I present an overview of chemometrics as it is practiced today. Receiving special emphasis are: the design of experiments to acquire information from the relevant universe of possible measurements; the establishment of relationships among independent and dependent variables; the importance of minimizing purely experimental uncertainty; sequential simplex optimization; analysis of principal components; and cluster analysis.

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23

FIGIEL, KERRY, and MICHAEL FORBES. "The evolution of reel statistical methods." June 2019 18, no. 6 (July 1, 2019): 365–77. http://dx.doi.org/10.32964/tj18.6.365.

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Multiple statistical methods for calculating the variance partition analysis (VPA) of reel data have existed for decades. In the paper industry, VPA is also commonly known as reel statistics. VPA commonly consists of total variance (TOT) that is then divided into three components: cross direction (CD), machine direction (MD), and residual (RES). A common mathematical procedure is referred to as ANOVA (analysis of variance). TAPPI Standard Test Method T 545 “Cross-machine grammage profile measurement (gravimetric method)” addresses paper testing and includes the ANOVA equations that have also been used to analyze scanning data. In the 1990s, TAPPI published TIP 1101-01 “Calculation and partitioning of variance using paper machine scanning sensor measurements,” which contained simple formulas that were easy to implement and could be used by a novice to generate statistics on a spreadsheet. All involved quality control system (QCS) suppliers agreed to support this common method in their QCS. TIP 1101 was recently revised, and this paper concerns the analysis of data collected from a scanning sensor in a QCS and the creation of a common method for the calculation of reel statistics by TAPPI’s Process Control Division.

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24

Güler, Cüneyt, Geoffrey D. Thyne, John E. McCray, and Keith A. Turner. "Evaluation of graphical and multivariate statistical methods for classification of water chemistry data." Hydrogeology Journal 10, no. 4 (May 9, 2002): 455–74. http://dx.doi.org/10.1007/s10040-002-0196-6.

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25

Svitanko, I. V., and T. S. Pivina. "Molecular modeling in synthesis: from statistical methods to quantum chemistry and practical applications." Russian Chemical Bulletin 73, no. 5 (May 2024): 1093–108. http://dx.doi.org/10.1007/s11172-024-4226-6.

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26

Bain, Alex D., Brian J. Fahie, Tomasz Kozluk, and William J. Leigh. "Improvements in the quantitation of NMR spectra by the use of statistical methods." Canadian Journal of Chemistry 69, no. 8 (August 1, 1991): 1189–92. http://dx.doi.org/10.1139/v91-177.

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In many applications of NMR spectroscopy to chemistry, quantitation play s a key role. This paper argues that peak heights not only can be used for quantitative work in NMR but that for careful work they should be used. Integrals are accurate, but because they depend on many subjective factors, their precision is suspect. Peak heights are very reproducible on modern spectrometers and so, given this precision, the accuracy can be obtained as well by using calibration methods. An example of the application of these methods is given in the case of a 13C labelling experiment that elucidates a photochemical transformation. Key words: NMR, labelling, quantitation, peak heights, integrals.

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27

Richardson, Thomas H. "Reproducible bad data for instruction in statistical methods." Journal of Chemical Education 68, no. 4 (April 1991): 310. http://dx.doi.org/10.1021/ed068p310.

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28

Waterman, Daniel S., Frank W. Bonner, and John C. Lindon. "Spectroscopic and statistical methods in metabonomics." Bioanalysis 1, no. 9 (December 2009): 1559–78. http://dx.doi.org/10.4155/bio.09.143.

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29

Zambrano, Felix, and Emanuel Munoz. "Statistical machine learning methods applied in the study of web accessibility: a literature review." Minerva 2023, Special (June 15, 2023): 97–105. http://dx.doi.org/10.47460/minerva.v2023ispecial.121.

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Technological development in solid-state chemistry, nanotechnology, and new materials is advancing at an accelerated pace; studies of methods to generate thin films of conductive and semiconductor materials are of great interest; however, current methods tend to be very expensive and inaccessible to developing countries. This work seeks to present viable and economical alternatives for teaching laboratories to investigate chemical deposition processes in aqueous solutions and produce thin layers of materials of interest in solid-state chemistry and new materials. The metals studied were copper, cobalt and, nickel in different salts and reducing agents, hydrazine hydrochloride, phenylhydrazine and, sodium borohydride. The main results show that it is possible to use cheaper chemicals to study depositions in an aqueous solution, a viable alternative for laboratories.

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30

Lynch, Joanna M., David M. Barbano, Patrick A. Healy, and J. Richard Fleming. "Performance Evaluation of the Babcock and Ether Extraction Methods: 1989 through 1992." Journal of AOAC INTERNATIONAL 77, no. 4 (July 1, 1994): 976–81. http://dx.doi.org/10.1093/jaoac/77.4.976.

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Abstract Results of the collaborative studies of the performance of the Babcock method (989.04) and the modified Mojonnier ether extraction method (989.05) for the determination of the fat content of raw milk were published in 1988. Method performances were characterized by using the harmonized International Organization for Standardization–International Union of Pure and Applied Chemistry–Association of Official Analytical Chemists (ISO/IUPAC/AOAC) guidelines for method validation, and the methods were approved official first action. During 1989 through 1992, the split-sample collaborative study format was used to monitor the performance of these methods as a part of an ongoing quality assurance program for a group of laboratories. Seven blind duplicate samples of raw milk were sent from a central laboratory once every 2 months to each participating laboratory (11 to 17 laboratories). Data were analyzed by using the same statistical procedures used in the 1988 study. Over time, both the within- and between-laboratory performances of both methods were as good as or, in most cases, better than the results from 1988. The data demonstrate that the statistical protocol for collaborative studies can be used effectively as the basis for a multilaboratory quality assurance program and that the method performance achieved in a collaborative study can be maintained and even improved with time.

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31

Brosius, Jan. "Statistical direct methods revisited. Solving the constraints." Zeitschrift für Kristallographie 227, no. 4 (April 2012): 190–98. http://dx.doi.org/10.1524/zkri.2012.1460.

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32

Lorber, Avraham, and Bruce R. Kowalski. "Numerical and statistical properties of target factor analysis methods." Analytical Chemistry 61, no. 10 (May 15, 1989): 1168–69. http://dx.doi.org/10.1021/ac00185a023.

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33

Hagan, Paul, and Dymphna Fellowes. "Multivariate statistical methods in battery research." Journal of Power Sources 122, no. 1 (July 2003): 77–84. http://dx.doi.org/10.1016/s0378-7753(03)00344-6.

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34

HURST, JANET B., and MICHAEL L. MILLARD. "Evaluation of alpha-SiC Sintering Using Statistical Methods." Journal of the American Ceramic Society 68, no. 7 (July 1985): C—178—C—181. http://dx.doi.org/10.1111/j.1151-2916.1985.tb10166.x.

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35

Christensen, Jan H., Asger B. Hansen, Ulrich Karlson, John Mortensen, and Ole Andersen. "Multivariate statistical methods for evaluating biodegradation of mineral oil." Journal of Chromatography A 1090, no. 1-2 (October 2005): 133–45. http://dx.doi.org/10.1016/j.chroma.2005.07.025.

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36

Miller, Jane C., and James N. Miller. "Basic statistical methods for analytical chemistry. Part I. Statistics of repeated measurements. A review." Analyst 113, no. 9 (1988): 1351. http://dx.doi.org/10.1039/an9881301351.

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37

McNeil, Vivienne H., and Myriam A. A. Raymond. "Mapping regional groundwater chemistry zones in the Fitzroy Basin, using statistical and conceptual methods." Proceedings of the Royal Society of Queensland 118 (October 2013): 37–61. http://dx.doi.org/10.5962/p.357778.

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38

Paksy, L. "Use of mathematical-statistical methods in spectrochemical quality control." Microchemical Journal 45, no. 3 (June 1992): 318–28. http://dx.doi.org/10.1016/0026-265x(92)90091-g.

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39

Kirk, Seth, Mark Strobel, Christopher S. Lyons, and Stuart Janis. "A statistical comparison of contact angle measurement methods." Journal of Adhesion Science and Technology 33, no. 16 (May 29, 2019): 1758–69. http://dx.doi.org/10.1080/01694243.2019.1611400.

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40

Hsieh, Eric, Chin-fu Hsiao, and Jen-pei Liu. "Statistical methods for evaluating the linearity in assay validation." Journal of Chemometrics 23, no. 1 (January 2009): 56–63. http://dx.doi.org/10.1002/cem.1194.

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41

Bucur, Elena, Andrei Vasile, Luoana Florentina Pascu, Carol Blaziu Lehr, and Gabriela Geanina Vasile. "Environmental Impact Assessment Regarding Indoor Air Quality Using Statistical Methods." Revista de Chimie 69, no. 11 (December 15, 2018): 3225–28. http://dx.doi.org/10.37358/rc.18.11.6718.

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This paper brings into attention of the indoor air specialists new information regarding the assessment of the potential cumulated impact of the air chemical compounds and microclimate factors on materials; for exemplification it was selected a wooden church made by oak and the monitoring values for temperature, humidity and the concentration of four chemical compounds with a destructive potential on organic materials generally: NO2, SO2, O3 and PM2.5.

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42

Haaland, David M. "Quantitative infrared analysis of borophosphosilicate films using multivariate statistical methods." Analytical Chemistry 60, no. 11 (June 1988): 1208–17. http://dx.doi.org/10.1021/ac00162a022.

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43

Efremenkov, V. V., and K. Yu Subbotin. "Statistical control methods in glass batch preparation process." Glass and Ceramics 63, no. 5-6 (May 2006): 139–41. http://dx.doi.org/10.1007/s10717-006-0061-5.

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44

Wei, Ai Hua, Feng Shan Ma, Dong Fei Yan, and Yu Feng. "The Salinization Problem of the Deep Groundwater Based on Multivariable Statistical Methods." Advanced Materials Research 594-597 (November 2012): 2520–24. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2520.

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To indentify the influence of human activities on groundwater chemistry, fifteen groundwater samples, from the mostly exploited aquifers, were collected at Tanghai County. Considering the multivariable statistical method is reliable to study the anthropogenic process affecting groundwater composition especially in the early stage, 10 measured hydrochemical variables were used in factor analysis and hierarchical cluster analysis. Factor 1 included EC, TDS, Cl-, and K+ is interpreted as relating to groundwater salinization. Factor 2 and factor 3 is mostly influenced by the water-rock interactions during the flow path and fertile contamination, respectively. Meanwhile, the Q-mode classification result reveals that the cluster 1 having high factor 1 scores also is related to the over-pumping of groundwater, in accordance with the factor analysis result. Generally, to protect freshwater resources and suitable development of this study, some related measures should be mitigated to limit groundwater mining.

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45

Brereton, RichardG. "Multivariate statistical methods: A primer, by Bryan F.J. Manly." Chemometrics and Intelligent Laboratory Systems 4, no. 1 (July 1988): 4–5. http://dx.doi.org/10.1016/0169-7439(88)80004-2.

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46

Lewis, Richard A. "Statistical methods for quality improvement, by Thomas P. Ryan." Chemometrics and Intelligent Laboratory Systems 12, no. 1 (November 1991): 98–99. http://dx.doi.org/10.1016/0169-7439(91)80116-8.

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47

Gennaro, M. C., C. Abrigo, E. Marengo, C. Baldin, and M. T. Martelletti. "Determination of creatinine in human serum. Statistical intercalibration of methods." Analyst 120, no. 1 (1995): 47. http://dx.doi.org/10.1039/an9952000047.

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48

Alouaamari, Mohamed, Michel H Lefebvre, Christiaan Perneel, and Michael Herrmann. "Statistical Assessment Methods for the Sensitivity of Energetic Materials." Propellants, Explosives, Pyrotechnics 33, no. 1 (February 2008): 60–65. http://dx.doi.org/10.1002/prep.200800210.

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49

Yoon, Ji Won, Simon Godsill, Eriks Kupče, and Ray Freeman. "Deterministic and statistical methods for reconstructing multidimensional NMR spectra." Magnetic Resonance in Chemistry 44, no. 3 (March 2006): 197–209. http://dx.doi.org/10.1002/mrc.1752.

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50

Stone, M. "Statistical analysis methods for corrosion mapping inspection data." Insight - Non-Destructive Testing and Condition Monitoring 53, no. 2 (February 2011): 76–81. http://dx.doi.org/10.1784/insi.2011.53.2.76.

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