Application of Mathematical Statistics Analysis Algorithm for Chemical Data
International Journal of Materials Science and Applications
Volume 6, Issue 6, November 2017, Pages: 297-301
Received: Dec. 5, 2017; Published: Dec. 6, 2017
Views 1423      Downloads 36
Shen Nana, Nanjing Chem Cyber Technology Company Ltd, Nanjing, China
Lu Xinjian, Nanjing Chem Cyber Technology Company Ltd, Nanjing, China
Article Tools
Follow on us
In this paper, chemical process data is analyzed by variance, the least square algorithm and then comparing the original data and processed data in excel. Through the comparing result, processed data is easier for operators to observe and find out rules and hidden problems in chemical conditions. According to the two algorithms, experienced operators can adjust chemical conditions to be normal. So they are better ways to optimize chemical conditions, as a result, the data analysis algorithm make a contribution to chemical industry.
Data Analysis, Excel, Least Squares Method, Chemical Conditions
To cite this article
Shen Nana, Lu Xinjian, Application of Mathematical Statistics Analysis Algorithm for Chemical Data, International Journal of Materials Science and Applications. Vol. 6, No. 6, 2017, pp. 297-301. doi: 10.11648/j.ijmsa.20170606.15
Couper, James R., W. Roy Penney, and James R. Fair. Chemical Process Equipment-Selection and Design (Revised 2nd Edition). Gulf Professional Publishing, 2009.
Macfarlane, Robert, et al. The NJOY Nuclear Data Processing System, Version 2016. No. LA-UR-17-20093. Los Alamos National Laboratory (LANL), 2017.
Weaver, Kathleen F., et al. "Basics in Excel." An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences, First (2018), pp. 523-537.
Parsons, Luke A., et al. "Temperature and precipitation variance in CMIP5 simulations and paleoclimate records of the last millennium." Journal of Climate 2017 (2017).
Pickles, Anthony J. "To Excel at bridewealth, or ceremonies of Office." Anthropology Today 33.1 (2017), pp. 19-22.
Jacobs, Perke, and Wolfgang Viechtbauer. "Estimation of the biserial correlation and its sampling variance for use in meta‐analysis." Research synthesis methods 8.2 (2017), pp. 161-180.
Duník, Jindřich, Ondřej Straka, and Miroslav Šimandl. "On autocovariance least-squares method for noise covariance matrices estimation." IEEE Transactions on Automatic Control 62.2 (2017), pp. 967-972.
Laboure, Vincent M., Ryan G. McClarren, and Yaqi Wang. "Globally Conservative, Hybrid Self-Adjoint Angular Flux and Least-Squares Method Compatible with Voids." Nuclear Science and Engineering 185.2 (2017), pp. 294-306.
Benelli, Giovanni. "Commentary: data analysis in bionano science—issues to watch for." Journal of Cluster Science (2017), pp. 1-4.
Tyanova, Stefka, et al. "The Perseus computational platform for comprehensive analysis of (prote) omics data." Nature methods 13.9 (2016), pp. 731-740.
Ammann, M., et al. "IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation." (2016).
Berger, Elisabeth, et al. "Field data reveal low critical chemical concentrations for river benthic invertebrates." Science of the Total Environment 544 (2016): 864-873.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931