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Programming Language and Big Data Applications
Published in Vijender Kumar Solanki, Vicente García Díaz, J. Paulo Davim, Handbook of IoT and Big Data, 2019
SAS is the leading analytical software on the market. The SAS system is divided into two main areas: procedures to perform analysis and fourth-generation language that allows users to manipulate data. This is known as the DATA step. The SAS system processes data in memory when possible and then uses system page files to manage data of any size efficiently. SAS software (with its language) is a famous and common solution for accessing, transforming, and reporting data by using its flexible, extensible, and Web-based interface. The SAS analytics platform consists of many analytical applications that form this application framework and make it a very useful tool for all data scientists in most of their tasks. The main useful analytical applications are the following: SAS Text Miner is a plug-in that can be added to the SAS Enterprise Miner environment, because it facilitates the main concept of text mining, which is the prediction aspect, and supports it with a very rich set of tools. SAS Text Miner is able to manipulate with different sources of textual data: Normal local text files.Retrieved text from SAS data sets or other external databases.Files on the Web.
Reporting and Forecasting
Published in Antonio Specchia, Customer Relationship Management (CRM) for Medium and Small Enterprises, 2022
SAS offers SAS Visual Analytics on its cloud-ready and microservices-based platform, SAS Viya. SAS Visual Analytics is one component of SAS's end-to-end visual and augmented data preparation, ABI, data science, ML and AI solution. SAS's extensive Viya-based industry, forecasting, text analytics, intelligent decisioning, edge analytics and risk management solutions use SAS Visual Analytics on Viya.
SAS
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
The SAS System is an integrated system of computer software products designed to allow for the easy analysis of data. The SAS system includes many components that perform many functions including database management, report writing, graphics, operations research, applications development, and statistical analysis. SAS, which stands for Statistical Analysis System, is foremost a software package devoted to statistical analysis.
Comparison of explosion puffing drying with other methods on the physicochemical properties and volatiles of yam (Dioscorea opposita thunb.) chips through multivariate analysis
Published in Drying Technology, 2021
Qi Gao, Jia-Nan Chen, Jian-Chao Zhang, Chun-Ju Liu, Da-Jing Li, Chun-Quan Liu, Masaru Tanokura, You-Lin Xue
All experiments were performed at least three times. Analysis of variance (ANOVA) on the data was performed using the general linear model (GLM) procedure in SAS software (SAS Institute Inc., NC, USA). Principal component analysis (PCA) was applied to evaluate the drying methods based on the physicochemical properties of the dried chips. Hierarchical cluster analysis (HCA) was applied to classify the drying methods, and orthogonal projection on latent structure-discriminant analysis (OPLS-DA) was applied to identify their characteristic properties. SIMCA 15 software (Umetrics, MKS Instruments Inc., Sweden) was used for the PCA, HCA and OPLS-DA analyses.
Optimum design of stenter machine hot air supply chamber by coupling CFD & DOE
Published in The Journal of The Textile Institute, 2023
Miguel Thomas Yaovi Adankpo, Zhong Xiang, Huang Ye Feng, Miao Qian
Since our research involves categorical and continuous factors, it might not be possible to construct an orthogonal design for screening main effects (Proust, n.d.). Based on the research of (Lekivetz et al., 2015) main effects screening design could be done because it allows for good balance properties based on the Chi-square criterion. As said earlier, JMP SAS software was used. The algorithm used to generate the design attempts to construct an orthogonal array of strength two (JMP, 2016). This type of orthogonal array allows orthogonal estimation of main effects when interactions are negligible.
Multivariate analyses of the physicochemical properties of turnip (Brassica rapa L.) chips dried using different methods
Published in Drying Technology, 2020
You-Lin Xue, Jia-Nan Chen, Hao-Ting Han, Chun-Ju Liu, Qi Gao, Jia-Heng Li, Da-Jing Li, Masaru Tanokura, Chun-Quan Liu
Data were statistically analyzed using analysis of variance and the general linear model procedure in SAS Version 8.0 (SAS Institute Inc., Cary, NC, USA). Differences with probabilities of 0.05 or less were defined as significant. PCA was used for the dried turnip chips based on their physicochemical properties. HCA and OPLS-DA were used to classify the data and study the correlation between physicochemical properties and the characteristics of the drying methods. PCA, HCA, and OPLS-DA analysis were performed using the SIMCA 15 Software (Umetrics, MKS InstrumentsInc, Sweden).