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.
| Published in | International Journal of Materials Science and Applications (Volume 6, Issue 6) |
| DOI | 10.11648/j.ijmsa.20170606.15 |
| Page(s) | 297-301 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Data Analysis, Excel, Least Squares Method, Chemical Conditions
| [1] | Couper, James R., W. Roy Penney, and James R. Fair. Chemical Process Equipment-Selection and Design (Revised 2nd Edition). Gulf Professional Publishing, 2009. |
| [2] | Macfarlane, Robert, et al. The NJOY Nuclear Data Processing System, Version 2016. No. LA-UR-17-20093. Los Alamos National Laboratory (LANL), 2017. |
| [3] | 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. |
| [4] | Parsons, Luke A., et al. "Temperature and precipitation variance in CMIP5 simulations and paleoclimate records of the last millennium." Journal of Climate 2017 (2017). |
| [5] | Pickles, Anthony J. "To Excel at bridewealth, or ceremonies of Office." Anthropology Today 33.1 (2017), pp. 19-22. |
| [6] | 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. |
| [7] | 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. |
| [8] | 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. |
| [9] | Benelli, Giovanni. "Commentary: data analysis in bionano science—issues to watch for." Journal of Cluster Science (2017), pp. 1-4. |
| [10] | Tyanova, Stefka, et al. "The Perseus computational platform for comprehensive analysis of (prote) omics data." Nature methods 13.9 (2016), pp. 731-740. |
| [11] | Ammann, M., et al. "IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation." (2016). |
| [12] | Berger, Elisabeth, et al. "Field data reveal low critical chemical concentrations for river benthic invertebrates." Science of the Total Environment 544 (2016): 864-873. |
APA Style
Shen Nana, Lu Xinjian. (2017). Application of Mathematical Statistics Analysis Algorithm for Chemical Data. International Journal of Materials Science and Applications, 6(6), 297-301. https://doi.org/10.11648/j.ijmsa.20170606.15
ACS Style
Shen Nana; Lu Xinjian. Application of Mathematical Statistics Analysis Algorithm for Chemical Data. Int. J. Mater. Sci. Appl. 2017, 6(6), 297-301. doi: 10.11648/j.ijmsa.20170606.15
AMA Style
Shen Nana, Lu Xinjian. Application of Mathematical Statistics Analysis Algorithm for Chemical Data. Int J Mater Sci Appl. 2017;6(6):297-301. doi: 10.11648/j.ijmsa.20170606.15
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Download@article{10.11648/j.ijmsa.20170606.15,
author = {Shen Nana and Lu Xinjian},
title = {Application of Mathematical Statistics Analysis Algorithm for Chemical Data},
journal = {International Journal of Materials Science and Applications},
volume = {6},
number = {6},
pages = {297-301},
doi = {10.11648/j.ijmsa.20170606.15},
url = {https://doi.org/10.11648/j.ijmsa.20170606.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmsa.20170606.15},
abstract = {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.},
year = {2017}
}
TY - JOUR T1 - Application of Mathematical Statistics Analysis Algorithm for Chemical Data AU - Shen Nana AU - Lu Xinjian Y1 - 2017/12/06 PY - 2017 N1 - https://doi.org/10.11648/j.ijmsa.20170606.15 DO - 10.11648/j.ijmsa.20170606.15 T2 - International Journal of Materials Science and Applications JF - International Journal of Materials Science and Applications JO - International Journal of Materials Science and Applications SP - 297 EP - 301 PB - Science Publishing Group SN - 2327-2643 UR - https://doi.org/10.11648/j.ijmsa.20170606.15 AB - 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. VL - 6 IS - 6 ER -
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